A causal comparative factorial analysis of factors

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4-2006

A causal comparative factorial analysis of factorsaffecting service level agreements in a U.S. Navyenterprise information systems networkJamie Lee Quintana

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A CAUSAL COMPARATIVE FACTORIAL ANALYSIS

OF FACTORS AFFECTING SERVICE LEVEL AGREEMENTS IN A

U.S. NAVY ENTERPRISE INFORMATION SYSTEMS NETWORK

by

Jamie Lee Quintana

Thesis

Submitted to the School of Engineering Technology

Eastern Michigan University

in partial fulfillment of the requirements

for the degree of

MASTER OF SCIENCE

in

Engineering Management

Thesis Committee:

Tracy Tillman, PhD, Chair

Robert Chapman, PhD

Hiral Shah

April 2006

Ypsilanti, Michigan

ii

ABSTRACT

This paper presents the results of a research study related to the Navy

and Marine Corps Intranet (NMCI). This study used MANOVA and one-way

ANOVA, including post hoc tests, to analyze data sets corresponding to service

level agreement metrics for over 300 Navy sites. Within the NMCI network,

factors size, server farm, Network Operations Center (NOC), area, region, and

group are affecting the performance metrics as defined in the service level

agreements (SLA). Each one of the factors is statistically disparate for at least

one SLA. Checks for normality indicate nonnormal behavior for most data sets.

The results, conclusions, and recommendations have been provided to Navy

service level managers to improve the system.

iii

TABLE OF CONTENTS

ABSTRACT ..................................................................................................... ii

LIST OF TABLES........................................................................................... .vi

LIST OF FIGURES ........................................................................................ vii

CHAPTER 1: INTRODUCTION AND BACKGROUND ....................................1

Introduction ...........................................................................................1

Statement of the Problem......................................................................1

Nature and Significance of the Problem................................................1

Objective ..............................................................................................2

Hypothesis ..........................................................................................3

Delimitations ........................................................................................4

Assumptions..........................................................................................5

Limitations ...........................................................................................5

Definition of Terms ................................................................................5

Summary...............................................................................................7

CHAPTER 2: REVIEW OF RELATED LITERATURE....................................8

Introduction ...........................................................................................8

Background Information ........................................................................8

Literature Related to the Problem .........................................................9

Literature Related to the Research Design .........................................19

Variables .............................................................................................21

Summary.............................................................................................24

iv

TABLE OF CONTENTS (Continued)

CHAPTER 3: RESEARCH DESIGN AND METHODOLOGY ........................25

Introduction .........................................................................................25

Research Design.................................................................................25

Research Setting.................................................................................30

Population and Sample .......................................................................31

Data Collection....................................................................................36

Variables .............................................................................................41

Data Analysis ......................................................................................44

Personnel ............................................................................................48

Required Resources ...........................................................................49

Timeline...............................................................................................50

Summary.............................................................................................52

CHAPTER 4: PRESENTATION AND ANALYSIS OF DATA .........................53

Introduction .........................................................................................53

Data Presentation ...............................................................................53

Data Analysis ......................................................................................55

Box Plot Analysis ................................................................................94

Summary...........................................................................................100

CHAPTER 5: RESULTS, CONCLUSIONS, AND

RECOMMENDATIONS.....................................................................102

v

TABLE OF CONTENTS (Continued)

Introduction .......................................................................................102

Results ..............................................................................................102

Conclusions.......................................................................................104

Recommendation ..............................................................................108

REFERENCES ............................................................................................109

APPENDICES..............................................................................................113

Appendix A: Analysis Summary Sheet..............................................114

Appendix B: Post-Hoc Summary Sheet ............................................118

Appendix C: Sample Box Plots .........................................................122

Appendix D: Sample Residual/Normal Probability Plots ...................124

Appendix E: Sample Nonnormal Probability Plot ..............................126

Appendix F: Coded Levels ................................................................128

Appendix G: Tukey’s Test Results ....................................................131

Appendix H: Variance Test Results...................................................151

Appendix I: Residual Plot Results .....................................................181

Appendix J: Box Plot Results ............................................................211

vi

LIST OF TABLES

Table Page

1 Test of Hypotheses ..............................................................................3

2 Transformed Data Sample .................................................................27

3 Sample Output Using Minitab’s One-Way ANOVA ............................27

4 Sample Output Using MANOVA.........................................................30

5 SLA Population and Sample ..............................................................32

6 SLA Collection and Measurement Methods .......................................37

7 Independent and Dependent Variables..............................................42

8 Factors and Treatments .....................................................................44

9 Allotted Budget...................................................................................50

10 Sample Data Presented for SLA101 ..................................................54

11 Significant Factor and Levels .............................................................55

12 ANOVA Results for S101.B1 (Site Group) .........................................57

13 ANOVA Results for S101.B3 (Server Farm).......................................57

14 MANOVA Results for S103.1.3 (NOC)...............................................58

15 ANOVA Results for S103.1.3 (Region) ..............................................59

16 ANOVA Results for S103.1.3 (Site Group).........................................59

17 MANOVA Results for S103.3.1 (NOC)...............................................60



20 MANOVA Results for S103.3.2 (Site Size).........................................62


vii

LIST OF TABLES (Continued)

Table Page


23 ANOVA Results for S103.3.2 (Server Farm) ......................................64

24 MANOVA results for S103.4 (NOC) ...................................................64

25 ANOVA Results for S103.4 (Region) .................................................65

26 ANOVA Results for S103.4 (Site Group)............................................66

27 ANOVA Results for S103.6.B1 (Site Group) ......................................66

28 ANOVA Results for S103.6.B3 (Region) ............................................67

29 ANOVA Results for S103.6.B3 (Site Group) ......................................67

30 MANOVA Results for S105.A (NOC) .................................................68

31 ANOVA Results for S105.A (Region) .................................................69

32 MANOVA Results for S105.B (NOC) .................................................69

33 MANOVA Results for S107.2.I3 (NOC and Area) ..............................70

34 MANOVA Results for S107.2.I3 (Server Farm) ..................................71

35 ANOVA Results for S107.2.PL1 (Site Group) ....................................72

36 MANOVA Results for S107.3.B (Site Size and NOC) ........................72

37 ANOVA Results for S107.3.B (Site Group) ........................................73

38 ANOVA Results for S107.3.B (Server Farm)......................................74

39 Tukey’s Results for Significant Factors ..............................................75

40 Summary of Significant Levels .........................................................103

41 Test of Hypotheses Results .............................................................104

viii

LIST OF FIGURES

Figure Page

1 Sample Tukey test ............................................................................29

2 Gantt Chart for thesis completion......................................................51

3 Tukey’s test for SLA S101.B1 for factor group ................................131

4 Tukey’s test for SLA S101.B3 for factor server farm .......................132

5 Tukey’s test for SLA S103.1.3 for factor region...............................133

6 Tukey’s test for SLA S103.1.3 for factor group part A .....................134

7 Tukey’s test for SLA S103.1.3 for factor group part B .....................135

8 Tukey’s test for SLA S103.3.1 for factor region part A ....................136

9 Tukey’s test for SLA S103.3.1 for factor region part B ....................137

10 Tukey’s test for SLA S103.3.1 for factor group part C.....................138

11 Tukey’s test for SLA S103.3.1 for factor group part D.....................139

12 Tukey’s test for SLA S103.3.1 for factor group part E .....................140

13 Tukey’s test for SLA S103.3.1 for factor group part F .....................141

14 Tukey’s test for SLA S103.3.1 for factor group part G.....................142

15 Tukey’s test for SLA S103.3.2 for factor site size............................143

16 Tukey’s test for SLA S103.3.2 for factor server farm.......................143

17 Tukey’s test for SLA S103.4 for factor group...................................144

18 Tukey’s test for SLA S103.6.B3 for factor region ............................145

19 Tukey’s test for SLA S103.6.B3 for factor group part A...................146

20 Tukey’s test for SLA S103.6.B3 for factor group part B...................147

21 Tukey’s test for SLA S105.A for factor NOC ...................................148

22 Tukey’s test for SLA S105.A for factor region .................................148

23 Tukey’s test for SLA S105.B for factor NOC ...................................149

ix

LIST OF FIGURES (Continued)

Figure Page

24 Equal variance test for SLA S101.B1 for factor group.....................151

25 Equal variance test for SLA S101.B3 for factor server farm............152

26 Equal variance test for SLA S103.1.3 for factor NOC .....................153

27 Equal variance test for SLA S103.1.3 for factor region ...................154

28 Equal variance test for SLA S103.1.3 for factor group ....................155

29 Equal variance test for SLA S103.3.1 for factor NOC .....................156



32 Equal variance test for SLA S103.3.2 for factor site size ................159



35 Equal variance test for SLA S103.3.2 for factor server farm ...........162

36 Equal variance test for SLA S103.4 for factor NOC ........................163

37 Equal variance test for SLA S103.4 for factor region ......................164

38 Equal variance test for SLA S103.4 for factor group .......................165

39 Equal variance test for SLA S103.6.B1 for factor group..................166

40 Equal variance test for SLA S103.6.B3 for factor region .................167

41 Equal variance test for SLA S103.6.B3 for factor group..................168

42 Equal variance test for SLA S105.A for factor NOC ........................169

43 Equal variance test for SLA S105.A for factor region ......................170

44 Equal variance test for SLA S105.B for factor NOC ........................171

45 Equal variance test for SLA S107.2.L3 for factor NOC ...................172

46 Equal variance test for SLA S107.2.L3 for factor area ....................173

x


Figure Page

47 Equal variance test for SLA S107.2.L3 for factor server farm .........174

48 Equal variance test for SLA S107.2.PL1 for factor group................175

49 Equal variance test for SLA S107.3.B for factor site size ................176

50 Equal variance test for SLA S107.3.B for factor NOC .....................177

51 Equal variance test for SLA S107.3.B for factor group....................178

52 Equal variance test for SLA S107.3.B for factor server farm...........179

53 Residual plots for SLA S101.B1 for factor group.............................181

54 Residual plots for SLA S101.B3 for factor server farm....................182

55 Residual plots for SLA S103.1.3 for factor NOC .............................183

56 Residual plots for SLA S103.1.3 for factor region ...........................184

57 Residual plots for SLA S103.1.3 for factor group ............................185

58 Residual plots for SLA S103.3.1 for factor NOC .............................186



61 Residual plots for SLA S103.3.2 for factor site size ........................189



64 Residual plots for SLA S103.3.2 for factor server farm ...................192

65 Residual plots for SLA S103.4 for factor NOC ................................193

66 Residual plots for SLA S103.4 for factor region ..............................194

67 Residual plots for SLA S103.4 for factor group ...............................195

68 Residual plots for SLA S103.6.B1 for factor group..........................196

69 Residual plots for SLA S103.6.B3 for factor region .........................197

70 Residual plots for SLA S103.6.B3 for factor group..........................198

xi


Figure Page

71 Residual plots for SLA S105.A for factor NOC................................199

72 Residual plots for SLA S105.A for factor region ..............................200

73 Residual plots for SLA S105.B for factor NOC................................201

74 Residual plots for SLA S107.2.L3 for factor NOC ...........................202

75 Residual plots for SLA S107.2.L3 for factor area ............................203

76 Residual plots for SLA S107.2.L3 for factor server farm .................204

77 Residual plots for SLA S107.2.PL1 for factor group........................205

78 Residual plots for SLA S107.3.B for factor site size ........................206

79 Residual plots for SLA S107.3.B for factor NOC .............................207

80 Residual plots for SLA S107.3.B for factor group............................208

81 Residual plots for SLA S107.3.B for factor server farm...................209

82 Box plots for SLA S101.B1 for factor group.....................................212

83 Box plots for SLA S101.B3 for factor server farm............................213

84 Box plots for SLA S103.1.3 for factor NOC .....................................214

85 Box plots for SLA S103.1.3 for factor region ...................................215

86 Box plots for SLA S103.1.3 for factor group....................................216

87 Box plots for SLA S103.3.1 for factor NOC .....................................217



90 Box plots for SLA S103.3.2 for factor site size ................................220


xii


Figure Page


93 Box plots for SLA S103.3.2 for factor server farm ...........................223

94 Box plots for SLA S103.4 for factor NOC ........................................224

95 Box plots for SLA S103.4 for factor region ......................................225

96 Box plots for SLA S103.4 for factor group.......................................226

97 Box plots for SLA S103.6.B1 for factor group..................................227

98 Box plots for SLA S103.6.B3 for factor region.................................228

99 Box plots for SLA S103.6.B3 for factor group..................................229

100 Box plots for SLA S105.A for factor NOC........................................230

101 Box plots for SLA S105.A for factor region......................................231

102 Box plots for SLA S105.B for factor NOC........................................232

103 Box plots for SLA S107.2.L3 for factor NOC ...................................233

104 Box plots for SLA S107.2.L3 for factor area....................................234

105 Box plots for SLA S107.2.L3 for factor server farm .........................235

106 Box plots for SLA S107.2.PL1 for factor group................................236

107 Box plots for SLA S107.3.B for factor site size................................237

108 Box plots for SLA S107.3.B for factor NOC.....................................238

109 Box plots for SLA S107.3.B for factor group....................................239

110 Box plots for SLA S107.3.B for factor server farm...........................240

1

CHAPTER I: INTRODUCTION

Introduction

This chapter introduces and describes the purpose of this study, including

the statement of problem, the nature and significance of the problem, and the

objective of the study. Hypotheses, delimitations, assumptions, and defined

terms are also provided.

Statement of the Problem

Within the NMCI network, disparate factors may be adversely affecting the

performance metrics as defined in the service level agreements.

Nature and Significance of the Problem

The Navy and Marine Corp Intranet (NMCI) is an enterprise-wide,

contractor-owned, contractor-operated (COCO) service delivery of information

systems such as voice, video, and data. These common core services are

required by the Navy and Marines in order to conduct business. When the NMCI

is fully implemented, the network will provide service to over 700,000 users

working throughout the United States and at military sites located in Puerto Rico

and Cuba.

The primary contract for NMCI was awarded to Electronic Data Systems

Corporation (EDS) in 2000. Since then, many modifications to the contract have

occurred. Originally, the contract stipulated 44 Service Level Agreements (SLA)

spread out over multiple service categories; however, in an effort to provide a

more realistic account from the end user’s perspective, the number of traceable

2

SLAs was reduced from 44 to 7.

In December 2004, a spreadsheet containing the SLA metric data for over

300 Navy and Marine Corps sites was compiled. The spreadsheet not only

captured the SLA performance data as per the contract’s specification, but also

provided demographic and service information for each site (called factors in this

study). Specifically, the spreadsheet identified each site by size, servicing server

farm, servicing network operations control (NOC), area, region, and responsible

site group. An updated spreadsheet containing data through September 2005

was analyzed.

Without the use of modern computers and statistical software to analyze

large sets of data, humans have a difficult time recognizing patterns, correlations,

or significant differences in the data. New insight can be gained by using

statistical software to analyze the SLA data. According to Kumar (1996),

“Knowledge of computers and the relevant programs is extremely important and

ultimately saves time” (p. 222). The results gained from this study will provide

NMCI managers with important information that may improve their current

service level management practices and overall network quality.

Objective

The purpose of this study was to investigate and make available new

information regarding the significance or impact that each of the six factors has

on each of the service level agreements. Each factor was tested against each of

the service level agreements, including subcategories. If a factor appeared to be

3

significant, then post hoc tests were run on that factor to determine which

treatment or level within the factor was statistically disparate.

Hypothesis

As shown in Table 1 below, this study tested the following hypotheses with

respect to each SLA:

Table 1

Test of Hypotheses

Factor Null (Ho) Alternative (H1)______________

Servicing NOC No difference Significant difference

Servicing server farm No difference Significant difference

Customer site size No difference Significant difference

Service area No difference Significant difference

Servicing region No difference Significant difference

Servicing site group No difference Significant difference

_______________________________________________________________

4

Delimitations

The following delimitations exist within this study:

1. This study did not attempt to identify, suggest, or otherwise investigate

whether SLAs are being met as per the NMCI contract.

2. This study did not include or examine extraneous variables that may have

affected the relationship between independent and dependant variables.

3. This study did not determine the root cause behind disparate factors or

specific treatments within the factors.

4. Only the six factors identified (size, server farm, NOC, area, region,

groups) in the SLA metric spreadsheet dated September 2005 were analyzed.

5. No additional factors were researched or analyzed.

6. The factors region, server farm, and site group were tested for significance

individually (no interaction tests).

7. The factors area, NOC, and site size were tested for significance together

(with possible interaction tests).

8. Additional SLAs, including transitional SLAs, were not considered in this

study.

9. There were not sufficient data to test SLAs associated with Mission Critical

or High End seats.

10. This study was limited to the SLAs associated with unclassified basic data

seats because classified, voice, and video seats are not yet widely available.

5

11. The study was limited to SLA metric data from Navy sites. SLA data

collected from Marine Corp sites were not analyzed.

12. The study was limited to sites that had quantitative SLA data values (i.e.,

percentage score or raw value).

13. The population under study was limited to Navy NMCI sites that were "cut

over" to the enterprise network.

Assumptions

It was assumed that both the federal government (The Program Executive

Office for Information Technology (PEO-IT) for NMCI) and EDS have allowed the

contents contained in this document to be displayed or posted in a public forum.

Additionally, it was assumed that the data are accurate, reliable, and unbiased

since this study relied on data that is generated and captured by others to meet

specifications outlined in a government contract.

Limitations

The following limitation exists within this study:

At the time of this study, only the following SLAs had adequate data sets

for performing an analysis: S101.B1, S101.B2, S101.B3, S103.1.3, S103.3.1,

S103.3.2, S103.4, S103.6.B1, S103.6.B2, S103.B3, S105.A, S105.B, S107.1.A,

S107.2.L2, S107.2.L3, S107.2.PL1, S107.3.A, and S107.3.B

Definition of Terms

ANOVA. Analysis of variance is a collection of statistical models and their

associated procedures that compare means by splitting the overall observed

6

variance into different parts. The initial techniques of the analysis of variance

were pioneered by the statistician and geneticist Ronald Fisher in the 1920s and

1930s and are sometimes known as Fisher's ANOVA or Fisher's analysis of

variance (Wikipedia, 2005).

COCO. This is an acronym for contractor owned, contractor operated. In

this study, EDS Inc. was the prime contractor responsible for the overall effort;

many subcontractors did much of the work.

Cutover. This is a term used when commands have fully transitioned to

the NMCI network.

Disparate. This is a word used throughout paper, meaning completely

different or distinct in quality or kind; entirely dissimilar (Morris, 1982).

DOF. The Degrees of Freedom is the maximum number of independent

comparisons between testable elements.

Heteroscedastic. This is a random variable characterized as having an

inconsistent variance.

Interaction Test. This is a procedure used to determine if one or more

factors, when combined, have an effect on an observation or response.

MANOVA. This acronym stands for Multivariate (or Multiple) Analysis of

Variance. Procedure similar to ANOVA used to test the correlation between two

or more variables.

PEO-IT. The Program Executive Office for Information Technology (PEO-

IT) is an organization responsible for life-cycle management and oversight of

NAVY Information Technology (IT) programs, projects, and initiatives. PEO-IT

7

monitors, evaluates, and ensures successful performance of those assigned

programs, projects, and initiatives (Program Executive Office, 2005).

Server Farm. This is a regionally located group of servers used to provide

enterprise-wide, mission-critical services, such as email, web access, and virus

protection.

SLA. This is an acronym for Service Level Agreement. NMCI defines an

SLA as “a written agreement between an IT service provider (see EDS NMCI)

and the IT client (DoN or Marine Corps), defining the key service targets and

responsibilities of both parties” (Electronic Data System Corporation, 2004, p.44).

Treatment. This is a term used to describe different conditions, groups, or

levels which can produce significant effects on an observed response.

Summary

This study investigated and made available new information regarding the

significance or impact that each of the six factors has on each of the service level

agreements. The root cause affecting any disparate factor was not investigated.

Statistical software was used to analyze the SLA data. The information learned

from this study will provide NMCI managers with important information that may

improve the overall network quality. The next chapter will provide additional

background regarding NMCI, information systems, intranets, and industry’s use

of ANOVA and service level agreements.

8

CHAPTER 2: BACKGROUND AND REVIEW OF LITERATURE

Introduction

This chapter provides additional information regarding the history and

possible future of NMCI. Also, a review of literature provides background

information regarding Information Systems and Intranets, Network Analysis and

Measurement, Network Design, and Service Level Agreements. Additionally, this

chapter includes a section that examines industry’s use of ANOVA to solve

problems and acquire new information.

Background Information

NMCI represents the largest Navy contract ever awarded. Its value is over

$8 billion. To put into perspective, the Navy spends almost a quarter of its

allotted Information Technology (IT) budget per year to pay for the service.

During the first few years following its inception, NMCI had its share of

implementation pains; however, recent customer satisfaction surveys suggest

that NMCI is working well but has room for improvement.

At the writing of this document, NMCI has not had a single, enterprise-

wide network disruption. Over 353 sites supporting over 260,000 users have

cutover to the network, which includes 113 large sites (> 250 seats), 190 small or

remote sites (24–250 seats), and 50 very small sites (< 24 seats). The network

has over 30 unclassified server farms and 4 servicing network operation centers

(NOCs). Current estimates suggest that when the network is fully implemented, it

9

will support over 700,000 users at more than 2,236 sites.

In September 2004, EDS and the Navy both agreed to a modification to

the contract that took the number of service level agreements from an

unmanageable 44 to a more realistic and manageable 7. The reason for the

change was to follow typical SLA agreements present in the industry while

improving user performance and expectations. Additionally, the modification

incorporated incentives as well as penalties based on the new arrangement.

Disparate factors (to include disparate treatments within each factor) can

affect the service level agreements in the NMCI network, so identifying these

factors would help NMCI meet its goal to provide a quality enterprise service. For

example, if the results of the study indicate no significant difference between the

factors, then this information is very important to the managers maintaining and

operating the network. It tells them that each of the groups, NOCs, etc., are

operating at the same level with no significant variance between them; therefore,

no changes are required. However, if statistically significant differences result

between the factors, then service level managers (in particular) will become

aware that a problem exists, and they can investigate the disparate factors on

their own accord. If a problem is not known, then the problem cannot be fixed.

Literature Related to the Problem

Information Systems

A collection of components working together and used to achieve a

common purpose is a defined as a system. Often, a change or failure in one

10

part of the system can affect the system as a whole. Information Systems are a

broad collection of procedures and personnel responsible for the capture,

management, and the distribution of data and information (Martin, Brown,

DeHayes, Hoffer, & Perkins, 2002, p. 316). As with any system, all components

(including both tangible and intangible) must be working properly or the system

can fail. Systems share seven basic elements:

1. The first element is a boundary; components inside and outside the

system must be clearly delineated.

2. The second element is the environment; the environment provides input to

the system to include constraints and includes everything else outside the

system.

3. The third element is input; examples of input required to be manipulated

by the system include resources such as data, material, supplies, and anything

else that gets consumed or is needed by the system.

4. The forth element is output; this element includes anything that is provided

to the environment as a product of the system.

5. The fifth element is components, which are often systems themselves; the

components or subsystems process inputs into intermediate forms or direct

outputs.

6. The sixth element is interfaces; within a system, this is the location where

the environment and the system meet or interact with each other.

11

7. The last element of a system is storage; this is the location where

information, energy, or materials are temporarily or permanently held until

needed (Martin et al., 2002, p.317).

According to Shelly, Cashman, and Rosenblatt (2001) the characteristics

of information systems are usually formed by asking specific questions regarding

the systems and its relation to its intended business operation. Shelly et al.

(2001) also stated that the critical questions should include but not be limited to

the following:

(a) Does this system interact with other systems?

(b) What are the system’s boundaries?

(c) Will the system handle specialized business needs?

(d) What size is the company, and what growth is forecast? (p. 9).

Intranets

The Internet, as we know it today, owes much of its success to the

Advanced Research Projects Agency Network (ARPANET), which dates back to

1969. The ARPANET, created by the U.S Department of Defense, was the first

network that was interconnected to serve a common purpose and share

information. Initially, only universities made use of the ARPANET until the

1980s. Transmission Control Protocol/Internet Protocol (TCP/IP), the

predominant pair of protocols used on the Internet, was developed during the

ARPANET project (Martin et al., 2002, p. 118). The Internet is an intricate Wide

Area Network (WAN), which spans the world (Dean, 2002). The Internet

12

provides services to whomever has access, and it is not regulated by

government agencies. Some of the services provided by the Internet include

electronic mail, or email for short, remote login, discussion groups, data sharing,

and web browsing.

In the 1990s, intranets emerged, and they owe much of their success to

the Internet (Oppenheimer & Bardwell, 2002). An intranet is essentially the same

as the Internet, but with restricted access. Intranets typically contain the same

elements and services provided by the Internet, but access to its resources is

generally provided to members within a company or organization. Typically, a

corporate firewall lies between access to the Internet and a business’s Local

Area Network (LAN). In some cases, authorized members can use dial-up

modem access or Virtual Private Networking (VPN) to access protected

resources behind a boundary firewall.

Today’s networks are more complex and integrated than networks in the

past. In order to stay competitive, many business units rely on tightly connected

intranets to process and share information between remote sites and corporate

headquarters.

Network Design

A network is defined as a type of relation linking sets of people, objects, or

events. The set of persons, objects, or events in a network are called actors or

nodes (Knoke & Kuklinski, 1982). The networks designed today are very

complex and require an integration of various disciplines. According to Taylor

13

(1998), network design is as complex, or more, than the architectural design of a

building.

In the design of a network, the following factors must be considered:

scalability, reliability, availability, and maintainability. The network foundation

should be able to scale easily as requirements change and grow. The network

must be reliable and robust so that users are not affected by failed components.

According to Condra (1993, as cited in Meeker & Escobar, 1998), reliability is

quality over time. Critical services provided by the network must also be

available to users whenever they require it. So careful planning of redundancy to

include Uninterruptible Power Supply (UPS) backup is crucial at the design

stage. And last, the network must be designed for maintainability. External test

equipment including proper management software to monitor network health is

important. "The easiest networks to maintain are those where maintenance was

thought of during the design phase" (Taylor, 1998, p. 159).

Network Analysis and Measurement

Proper operation of the network and early problem identification are

critical, especially when SLAs (which often carry penalties) can be affected by a

system outage. Taylor (1998) recommended writing down at least ten different

failure scenarios with proposed solutions. These procedures could be viewed as

contingency plans or continuity of service plans. Proper and prompt

implementation of the plans will mitigate customer outages.

14

Today, most people not only expect technology to work, but they also

expect technology to work well. There is an increasing expectation for network

availability to be 100% (Massam, 2003). In order to adequately manage service

levels from the customer’s point of view, real-time monitoring is required. Few

management applications, because of their limited scalability, work well

enterprise wide, so end-to-end application monitoring fails to provide the actual

user view. No single tool or application can be used to determine problems

across a complex and integrated enterprise network. Thottan and Ji (1999)

contended that commercial management software cannot detect subtle changes

in the network which can affect performance; instead, it can only detect critical

failures such as a broken link or loss of link capacity. Statistical methods, custom

algorithms, and industry standard protocols like Management Information Bases

(MIBs) can improve network fault detection.

Service Level Agreements

Service Level Agreements (SLAs) are becoming more and more popular

in today’s business relations. Not surprisingly, SLAs serve not only the customer,

but also the service provider. In general, SLAs are a minimally negotiated level of

service, so there are usually no incentives for a provider to provide superior

service. For this reason, the development and negotiations of SLAs are critical.

According to Larson (1998), many factors are important when negotiating and

defining SLAs. Each of the SLAs should contain elements that can be measured

and managed, audited, provided at an economic rate, and maximize value to the

15

users. Additionally, each of the service level elements should contain specific

detailed components that define the service description, constraints, performance

measures, and pricing considerations. Similarly, Pratt (2003) suggested the

following key elements: establish a clear purpose, define key targets, establish

constraints, measure success, and use best practice. Other important

considerations include top management commitment, participated approach,

customer input, management framework, and involvement of junior staff.

However, SLA negotiation has its challenges; for example, some problems

continue to affect SLA success. First, SLAs are typically generated from scratch

because no industry standard is available. This can affect workload and inhibit

SLA creation. Second, service providers may use jargon that is unclear or not

easily recognized. Third, contracts that miss critical SLA parameters can lead to

lawsuits, tarnish reputations, and even cause economic loss. Building an SLA

Template library may prevent many of the problems affecting SLAs in the

industry, according to Tie and Luoming (2003).

SLAs are effective because they encourage IT organizations to

collaborate with their customers in making formal agreements. Additionally,

SLAs clarify responsibilities and build trust. However, some SLAs are not

successful. Many factors affect an SLA’s usefulness and degree of success. For

example, a limited-scope, less comprehensive SLA is more effective than an

overly complex one, and SLAs should only track the fewest numbers of

indicators, such as availability, reliability, responsiveness, and turnaround time

(Karten, 2004).

16

Managing and maintaining SLAs are equally important to defining clear

and concise SLAs. On the basis of a survey conducted by Infonetics in 2000,

titled User Plans for Network Management, Massam (2003) reported that the

importance of service level management was ranked tenth in a list of 13 SLA

factors. According to the results of the survey, service level management is not

very important. Interesting enough, the same survey reported network

availability, or response time, as the most important SLA factor. Management of

SLAs relies on intervention from both humans and machines. D’Arienzo,

Esposito, Romano, and Ventre (2003) addressed the need for automatic SLA

management and said, “There are no automatic processes for the

implementation of the negotiated SLAs which thus have to be instantiated by

manual intervention, and of course at a high cost” (p. 1402). They envisioned an

entity called an Elastic Network Node (SLA Manager), which would be

strategically placed between two networks. The node would then perform

statistical analysis of traffic flows (either short term or long term) as dictated by

the SLA’s requirements.

According to Pratt (2003), there are some disadvantages to SLAs. First,

appropriate levels for each of the service levels may not be viewed equally.

Second, as the quality of service increases, so does the customer’s expectation.

Similarly, Parish (1997) said that bureaucratization can be a problem: Because

SLAs cost businesses money to execute, additional SLAs may be assigned in

other production areas unnecessarily. In addition, defensiveness can be a

17

problem: Some companies may lose focus on the customer’s needs in an effort

to meet SLAs.

With regard to monitoring SLA levels, Kenyon (2002) listed the following

components of a good network monitoring system for monitoring SLAs: data

collection model, WAN interfaces, external data feeds, and predictive features,

including trend analysis and what-if scenarios, traffic shaping, data capture and

storage, reporting features, and diagnostic features. As for specific SLA metrics

to monitor, Kenyon found that no industry standard exists, but he suggested the

following as being important: network availability, circuit error rates, throughput,

network latency, and circuit stability.

Literature Related to the Research Design

Method

The recommended steps required for a research design containing

hypotheses include the following: generating hypotheses, formulating test

implications, formulating a research design, collecting data, analyzing, testing,

and synthesizing (Grove & Seesing, 1991).

Kumar (1996) suggested that manually analyzing data without the use of a

computer is only useful for calculating frequencies or simple cross calculations.

Data should be analyzed with a computer because manually analyzing data can

be very time consuming. Because this research study contains such a large

number of runs, the use of statistical software was recommended. Additionally,

Kumar stated that computers not only increase the speed at which work can be

18

done but also solve complex statistical and mathematical problems. Similarly to

Kumar, Montgomery and Runger (2003) said that computers coupled with

statistical methods are used to solve problems. So, given the number of factors

and the number of runs associated with each SLA, analysis of the data using a

computer and statistical software is not only recommended but also warranted. It

must be noted, however, that, according to Spirer, Spirer, and Jaffe (1998),

computers must be used with caution because they can affect the data during a

malfunction or perform improperly without the user’s knowledge. The errors

caused by computers are often difficult to find.

Analysis of Variance

Analysis of Variance (ANOVA) was first used by Ronald Fisher in the

1920s and 1930s and is sometimes referred to Fisher’s ANOVA (Wikipedia,

2005).

The ANOVA design proposed in this paper uses a popular approach

called “one-factor-at-a-time” (Montgomery, 2001, p. 3). Many problems can be

solved or analyzed using the one-factor-at-a time method. For example, Kundu

(2004) used a one-way ANOVA design to study a sample of 274 executive

responses in order to assess the impact of computer disasters. The study found

significant factors. Similarly, ANOVA was used in a study reported by Taslak

(2004) in the European Business Review titled “Factors Restricting Success of

Strategic Decisions.” The study sampled 200 randomly selected textile firms.

Questionnaires were filled out and collected. The results obtained from the

19

questionnaires were then analyzed the ANOVA method, followed by an

appropriate post hoc test. The results of ANOVA found significant factors to

include significant differences between firms using post hoc tests. Similar to the

analysis approach in this research, Raghunathan, Rao, and Solis (1997),

operationalized and then analyzed the practices of quality management, using

ANOVA and Tukey’s post hoc test. Tukey’s test showed significant differences in

some cases and none in others. An important point learned from this study is the

fact that a factor can be statistically significant with post hoc test reporting no

disparate means.

Because the design used factors that are considered unbalanced (the

number of observations per treatment are not equal), the test statistic is sensitive

to small departures from the assumption of equal variances. However, according

to Bathke (2005), the F-test is still valid for heteroscedastic data in some

balanced designs. In an effort to minimize Type I errors due to unequal

variances, Bartlett’s test for unequal variances was run for each factor found

significant at the 95% confidence level.

Strengths of ANOVA

According to Montgomery (2001), for testing the equality of several

means, as in this study, the best test is analysis of variance, or ANOVA.

Montgomery said that ANOVA is one of the most useful techniques in the field of

inferential statistics. ANOVA is robust and can handle moderate departures from

the normality assumption. ANOVA is the most appropriate choice for the study

20

because the factors (a) region, (b) server farm, and (c) site group should be

tested for equality of means one factor at a time. Also, each factor contains a

single categorical predictor variable measured on a continuous scale with

multiple levels, so the factors are well suited for analysis using ANOVA.

Additionally, and with minor modifications, ANOVA can be used with unbalanced

designs in which the number of observations per level is not equal.

Limitations of ANOVA

Montgomery (2001) stated the major disadvantage to this design is that it

fails to take into consideration the interactions between testing factors. Also,

ANOVA alone will not provide information about which level within the factor is

different; additional tests are required.

Multivariate Analysis of Variance

Multivariate Analysis of Variance (MANOVA) is a modified version of one-

way ANOVA. It takes into consideration cross-product covariance between

variables as well as each group’s means. Similar to one-way ANOVA, the

assumption of equal variance as well as normality is required. Although

MANOVA is fairly robust to departures from these assumptions, checks for

violation of these assumptions should occur. Also, in general, as the

dimensionality within MANOVA increases, robustness decreases (Rencher,

2002, p.198).

21

Strengths of MANOVA

Unlike the single-factor analysis of variance, MANOVA is able to test

multiple variables, including possible interactions, at the same time. MANOVA is

the most appropriate choice for this study because MANOVA has the capability

to test the factors area, NOC, and site size for significance, together with

possible interactions. By doing so, Type 1 errors are mitigated for these factors

because they are tested simultaneously, and the chance of identifying the most

important factor is increased.

Limitations of MANOVA

Similar to ANOVA, MANOVA will not provide information about which level

within the factor is different. Additional tests are required. Also, MANOVA is not

robust to variables that may be collinear or depend on one another. MANOVA

will detect—globally—if one or more levels yield significantly different results.

Multiple-range tests (a series of all possible pairwise t tests) can then be

employed to determine which levels (pairwise) are significantly different.

Variables

Variables are derived from the fact that particular characteristics may vary

among the units in a population (McClave & Sincich, 2000). If researchers

understand the relationship between one or two variables, then many problems

can be examined or explored. Many types of variables exist within an

experiment or real-life problem. For example, if extraneous variables are left

unmeasured, they may affect the degree of the cause-and-affect relationship

22

between the independent and dependent variables (Kumar, 1996, p. 51).

Additional considerations include noise, random, and intervening or confounded

variables. Often, a single dependent variable depends on multiple independent

variables as in this study. In total, there are six independent variables.

Measuring Scale Selection

One popular saying goes, “If you can measure it, you can manage it.” Still

another says, “If you can measure it, you can improve it.” Kumar (1996) stated

that measurement is critical to scientific research. In order to understand the

extent of variation, concepts should be operationalized in terms that are

measurable. The four measurement categories include the nominal, ordinal or

ranking, interval, and continuous scales. Examples of the nominal scale include

gender, political party preference, and religious choice. Examples of the ordinal,

or ranking, scale include socioeconomic classes, income with respect to average,

and attitudes (i.e., favorable or not). Examples of the interval scale include

temperature ranges and attitudinal scales (i.e., 10-20, 21-30, etc.). And finally,

examples of the continuous scale include continuous values such as height,

income, age, and weight (Kumar, 1996). The continuous scale was used

because the data for this study had been precollected and assigned either

percentage values or raw scores in decimal form.

Variation

In most designs, variation is essential to finding differences in techniques

or applications when testing variables. Understanding all sources of variation, as

23

well as being able to control or minimize background noise, is essential to

experimentation. According to Spirer et al. (1998) researchers use statistical

measurements, like the standard deviation, which displays the spread (or

dispersion) of the data, and variance, which is the square of the standard

deviation. Other forms of variation exist within an experiment. In this study, the

random error component of the linear model contains all other sources of

variability, including variability from uncontrolled factors.

Plotting histograms of residuals is typically done to test the independence

assumption. Similarly, a normal probability plot of the residuals is sometimes

useful in validating the normality assumption. The plot should resemble a straight

line and contain few to no outliers. The presence of outliers can affect the

analysis and may be cause for investigation (Montgomery, 2001, pp. 77-78).

Appendices C and D display sample plots similar to those used to analyze the

model's adequacy. The box plots in Appendix C display a few outliers. The plots

in Appendix D are considered normal and have few outliers. Appendix E displays

a normal plot of nonnormal data. The data are considered nonnormal because

the data plots are not linear.

Processing the Data

Knowledge of statistics is vital to understanding the relationship between

variables, especially when there is more than one variable (Kumar, 1996). The

first step in processing data includes editing the raw data taken from interviews,

questionnaires, observations, or secondary sources. The second step entails

24

coding, which includes developing a code book, pretesting the code book, coding

the raw data, and then verifying the coded data for accuracy. The third and last

step is the analysis, which includes developing a frame of analysis and then

doing the analysis, whether by computer or manually. Kumar (1996) suggested

using a computer to handle complicated statistical and mathematical produces

(p. 223).

Summary

The chapter provided additional details regarding the history and future of

NMCI. Information systems were viewed as a broad collection of procedures

and personnel responsible for the distribution of data and information. So, any

failure in any part of the system can affect the system as a whole. SLAs can be

effective, and managing and maintaining SLAs were shown to be equally

important as defining clear and concise SLAs. ANOVA, MANOVA, and post hoc

tests were shown to be effective and useful in determining the significance of

factors and treatments in a variety of applications. And finally, statistics, coupled

with an appropriate measuring scale, are crucial to understanding the

relationship between variables. Chapter 3 discusses the research-design

specifics related to both ANOVA and MANOVA, including the population under

study.

25

CHAPTER 3: METHODOLOGY

Introduction

This chapter provides information regarding the research design,

research setting, population, and sample size. Data collection methods including

data analysis, personnel, resources, budget, and timeline will be discussed.

Research Design

The type of design associated with this study is called causal-comparative,

as this researcher neither gathered nor controlled the data to be analyzed. This

type of research, as it relates to the topic under study, is considered to be

quantitative and applied in nature. The intent of this design is to determine

whether a significant relationship exists between each of the factors and

observations.

Three of the six factors appeared to be linear combinations of one

another. These factors are region, server farm, and site group. Because they are

possibly collinear, each of these factors was tested for significance individually.

For these factors, the researcher used one-way ANOVA and then an appropriate

post hoc test only if the F-test was found to be significant at the 95% confidence

level. The other three factors were not linear combinations of one another. These

factors were area, NOC, and site size. For these factors, the researcher used

MANOVA to test for significance and then an appropriate post hoc test only if the

F-test was found to be significant at the 95% confidence level. Examples of post

26

hoc tests include Tukey’s, Tukey-Kramer, Fisher’s, Scheffe’s, Bonferroni,

Dunnett’s, Duncan’s, and Newman-Keuls.

Previous checks of residual plots indicated that the SLA data was

nonnormal. Appendix E displays residual plots using nontransformed SLA data

(S101a). According to Mendenhall and Sincich (1995), if the distribution of

residuals departs greatly from normality, a normalizing transformation can be

used. Examples of such transforms include log(y), √y, and arcsin(y), where y is

the response variable. After many pretests, plots, and checks for the normality of

residuals using various transforms, arcsin appeared to work best overall in an

effort to “normalize” the SLA data.

This researcher configured Minitab® release 14 software for one-way

ANOVA to analyze sample SLA data for the factor server farm. As required, the

researcher applied an arcsin function to the sample SLA data 101a, which is now

transformed as shown Table 2. In doing so, checks for normality can be

accomplished. Table 2 displays both the original data and the transformed data.

The transformed values are termed T101a.

The arcsin transform is often used to transform percentages and is

defined below:

)aSaSaT 101( sin )101(arcsin 101 1/2-1==

27

Table 2 Transformed Data Sample

1.412020.975REMOTE S/ SWSOUTHWESTWestSDNIFALNS

1.18320.85714REMOTE S/ SWSOUTHWESTWestSDNISDNIVSSD

1.167740.84615REMOTE MW/ NENORTHEASTEastNRFKMECHS

1.339320.94737REMOTE SESOUTHEASTEastNRFKNWORS

1.18320.85714REMOTE SESOUTHEASTEastNRFKCHRLS

1.353420.95349REMOTE MW/ NENORTHEASTEastNRFKNWORS

1.57081REMOTE MW/ NENORTHEASTEastNRFKLKHRS

T101aS101aSite GroupRegionAreaNOCServer FarmSite Size

1.412020.975REMOTE S/ SWSOUTHWESTWestSDNIFALNS

1.18320.85714REMOTE S/ SWSOUTHWESTWestSDNISDNIVSSD

1.167740.84615REMOTE MW/ NENORTHEASTEastNRFKMECHS

1.339320.94737REMOTE SESOUTHEASTEastNRFKNWORS

1.18320.85714REMOTE SESOUTHEASTEastNRFKCHRLS

1.353420.95349REMOTE MW/ NENORTHEASTEastNRFKNWORS

1.57081REMOTE MW/ NENORTHEASTEastNRFKLKHRS

T101aS101aSite GroupRegionAreaNOCServer FarmSite Size

Note. S101a data was transformed using Minitab’s built-in calculator function.

Table 3 shows Minitab’s output for one-way ANOVA, using sample SLA data and

the factor Server Farm. Appendix D displays residual plots of transformed data.

Table 3 Sample Output Using Minitab’s One-Way ANOVA

Note. In the above sample, a P-value of .006 indicates the factor, Server Farm, is significant to

the (1 - 0.002) x 100 = 99.4% confidence level. Therefore, a post hoc test would be warranted

because one or more levels is statistically significant.

One-way ANOVA: T101a versus Server Farm Source DF SS MS F P

Server Farm 24 1.5840 0.0660 1.94 0.006

Error 272 9.2624 0.0341

Total 296 10.8464

S = 0.1845 R-Sq = 14.60% R-Sq(adj) = 7.07%

28

Minitab® release 14 only supports Tukey’s, Fisher’s, Dunnett’s, and Hsu’s

pairwise tests. According to Montgomery (2001), Tukey's test will determine

which levels are disparate within a factor. Tukey’s test makes use of the

studentized range statistic. However, for unequal samples, as in this study,

Tukey's test becomes the Tukey-Kramer test. If the normal assumption appears

to be in question, Levene’s test should be used because its procedure is robust

to departures from normality (Montgomery, 2001, p .82). Because Levene’s test

is not an option in Minitab, Tukey’s family error rate set to 95% mitigates the

making of Type 1 errors.

Figure 1 depicts an example of Minitab output following Tukey’s pairwise

test using sample SLA and the factor server farm. Because zero (on the number

scale) is included in the 95% confidence intervals for all pairwise comparisons,

there is not enough evidence to conclude that any of the factor’s levels are

significant. Therefore, no levels are disparate for this factor.

With regard to the box and residual plots, a few outliers were present, but

they do not appear to severely distort the analysis. Both ANOVA and MANOVA

are robust to small departures from normality.

29

Figure 1. Sample Tukey test.

Minitab® release 14 was configured to analyze the factors “Area,” “NOC,”

and “Site Size” using sample-normalized SLA data with MANOVA. By default,

Minitab uses a general linear model (GLM) (which is often employed where the

data is nonnormal) for the MANOVA analysis.

Server Farm = MILL subtracted from:

Server

Farm Lower Center Upper ---------+---------+---------+---------+

MUGU -0.4631 -0.1272 0.2087 (-----*------)

NRFK -0.4650 -0.1291 0.2068 (-----*------)

NWOR -0.2715 0.0136 0.2987 (----*-----)

OCEN -0.5438 -0.0360 0.4719 (---------*---------)

ORLO -0.7380 -0.0661 0.6057 (-------------*------------)

PAXR -0.3592 -0.0000 0.3591 (------*------)

PHIL -0.3494 -0.0424 0.2646 (-----*-----)

PRLH -0.5105 -0.1892 0.1320 (-----*------)

PRTH -0.3641 0.0757 0.5156 (--------*-------)

SDNI -0.2911 -0.0084 0.2744 (-----*----)

SDNS -0.4068 -0.0709 0.2650 (------*-----)

SMTH -0.8332 -0.1613 0.5105 (-------------*------------)

SPSC -0.4830 -0.1064 0.2703 (-------*------)

WNYD -0.4121 -0.1189 0.1744 (-----*----)

---------+---------+---------+---------+

-0.50 0.00 0.50 1.00

Server Farm = MUGU subtracted from:

Server

Farm Lower Center Upper ---------+---------+---------+---------+

NRFK -0.3129 -0.0019 0.3091 (-----*-----)

NWOR -0.1145 0.1408 0.3961 (----*----)

OCEN -0.4005 0.0912 0.5830 (---------*---------)

ORLO -0.5986 0.0611 0.7208 (------------*------------)

PAXR -0.2088 0.1272 0.4631 (------*-----)

PHIL -0.1947 0.0848 0.3643 (-----*----)

PRLH -0.3571 -0.0620 0.2330 (-----*-----)

PRTH -0.2182 0.2029 0.6240 (-------*-------)

SDNI -0.1338 0.1188 0.3715 (----*----)

SDNS -0.2547 0.0563 0.3673 (-----*-----)

SMTH -0.6939 -0.0341 0.6256 (------------*-------------)

SPSC -0.3338 0.0208 0.3754 (------*-------)

WNYD -0.2560 0.0083 0.2727 (----*----)

---------+---------+---------+---------+

-0.50 0.00 0.50 1.00

30

Table 4 shows only a single transformation of the response (SLA101)

tested. However, during the actual analysis, all associated transforms (each

response) were tested simultaneously.

Table 4 Sample Output Using MANOVA

Note. According to the above MANOVA output, no factors are significant at the 95% confidence

level. Each of the P-values for NOC, Area, and Site Size are > .05. Therefore, additional post

hoc tests are not required.

Research Setting

Independent parties, either working for the government as civilian

employees or contracted by the government to fulfill requirements defined in the

NMCI contract, collected all the data used in this study. All SLAs were collected

via electronic means with a frequency and sampling requirement defined by the

General Linear Model: T101a versus NOC, Area, Site Size Factor Type Levels Values

NOC fixed 3 NRFK, PRLH, SDNI

Area fixed 2 East, West

Site Size fixed 3 L, S, VSSD

Analysis of Variance for T101a, using Adjusted SS for Tests

Source DF Seq SS Adj SS Adj MS F P

NOC 2 0.07253 0.02482 0.01241 0.34 0.714

Area 1 0.00601 0.00349 0.00349 0.10 0.758

Site Size 2 0.08157 0.08157 0.04079 1.11 0.331

Error 291 10.68627 10.68627 0.03672

Total 296 10.84638

S = 0.191631 R-Sq = 1.48% R-Sq(adj) = 0.00%

31

NMCI contract. This researcher used data analysis software to examine the

collected data.

Population and Sample

The population under study was limited to only Navy NMCI sites that had

been "cut over" to the enterprise network. With a total of 2236 sites worldwide,

340 Navy sites had transitioned to NMCI and were a part of this study. The

confidence interval is ± 5% on the basis of the following equations at the 95%

confidence level:

(a) ∆

−⋅⋅=

2

22/

0

)1( ppzn

α = 385~05.0

)5.01(5.01.962

2−⋅⋅

(b) Zα/2 = 1.96 at the 95% level.

(c) p = 0.5 for participation level. p = 0.5 maximizes the samples required.

(d) ∆ = 0.05 target confidence interval.

The correction for a finite population using N = 340 (total NMCI population

cutover) is

)/(1 0

0

Nn

nn

+= = 181~

)1324.1(1

385

+

Therefore, a minimum of 181 data points for each SLA was required to

accurately represent the entire population at the 95% confidence level.

32

With regard to the collected SLA data, the sampling technique used to

determine particular SLA varies per SLA and is defined in attachment 2 of

contract N00024-00-D-6000 (Navy Marine Corps Intranet, 2004). The collection

frequency for all SLAs is monthly. Table 5 provides a brief description of how

each SLA was sampled.

Table 5 SLA Population and Sample _______________________________________________________________________

Sample

SLA Description Population Size Unit_________________

101 End-User All Navy All Tickets Closed External Incident

Problem Ticket

Resolution

102 Network All Navy All Tickets Open & Closed External

Problem Incident Tickets

Resolution

103.1.1 User E-mail All Navy Site24 Client

Availability

103.1.2 E-Mail All Navy TBD Client

End-to-End

103.1.3 E-Mail All Navy All servers Server

Server service

Availability

33

Table 5 (continued)

_______________________________________________________________________

Sample


103.1.4 E-mail All Navy Site24 Client

Client

Responsiveness

103.2 Web and All Navy TBD Client

Portal

Services

103.3.1 File All Navy All servers File Share Server

Server

Availability

103.3.2 File Share All Navy Site24 Client

Client

Responsiveness

103.3.4 Print All Navy All servers Print Server

Services

103.3.5 Network All Navy 10 Selected Test Account

PKI Logon sites, rotated

Services monthly

34

Table 5 (continued)

_______________________________________________________________________

Sample


103.3.6 Problem All Navy All Tickets Closed External Incident

Resolution Ticket

Government

Applications

103.7.1 RAS All Navy One Rep. RAS Access Point

Service per access

Availability point

103.7.2 RAS All Navy One Rep. Client

Client per access

Responsiveness point

103.8 Blackberry All Navy All BES BES Server

Services servers

104.1.1 Speed to All DON All calls End User calls to

Answer Help Help Desk

Desk Calls

104.1.2 Ave. Email / All DON All calls End user calls and emails

Voice mail and emails to Help Desk

Response

35

Table 5 (continued)

_______________________________________________________________________

Sample


104.2 Call All DON All calls End User calls to

Abandonment Help Desk

Rate

104.3 First Call All Navy All Tickets Closed Internal Incident

Resolution Ticket

105 Move, Add, All Navy All Requests MAC Change Request

Change

106.1 Security All Navy 100 Red Security Events

Event Team Created

Detection Events

106.2 Security All Navy Detection of Security Events Reports

Event Red Team

Reporting Created Events

106.3 Security All Navy All Reported Security Event

Event Contractor

Response Reported Events

36

Table 5 (continued)

_______________________________________________________________________

Sample


106.4 Config. All Navy <25,000 Designated Components

Management Workstations

Per month

107.1 Availability All Navy All Sites Inner Router

107.2 Latency / All Navy 120 Selected Site

Packet Loss Sites

107.3 Voice and All Navy 20 Selected Site

Video QOS Sites

________________________________________________________________

Note. The following statements apply to all SLAs excluding 106.1, 106.2, and 106.4:

(a) The contractor is responsible to collect the data. (b) Site24 in sample size column means the

following: Sites ≤ 24 will have two on-site representative points; Sites < 24 will not be measured

unless mutually determined by government and contractor. The following statement applies to

SLAs 106.1, 106.2, and 106.4: The government is responsible for collecting the data.

Data Collection

This study used precollected, existing data. The data were considered to

be reliable, accurate, and unbiased. Only the latest revision of data was

37

analyzed. Table 6 provides information related to SLA data collection and

measurement methods.

Table 6 SLA Collection and Measurement Methods ______________________________________________________________________

Measurement

SLA Description Frequency Method Formula_____________

101 End-User Continuous Incident Completed Closed

Problem Reports / Total Reports

Resolution

102 Network Continuous Incident Completed Closed

Problem Calls / / Total Open

Resolution Tickets

103.1.1 User E-mail TBD Automated Varies per site

Availability

103.1.2 E-Mail Continuous TBD Successes/ attempts

End-to-End

103.1.3 E-Mail Continuous Automated Varies per site

Server service

38

Table 6 (continued)

______________________________________________________________________

Measurement


Availability

103.1.4 E-mail TBD Automated Successes/ attempts

Client

Responsiveness

103.2 Web and TBD Automated Successes/ attempts

Portal

Services

103.3.1 File Continuous Automated Varies per site

Server

Availability

103.3.2 File Share Every 5 Automated Responses/ attempts

Client Minutes

Responsiveness

103.4 Print Continuous Automated Varies per site

Services

103.5 Network 0800 to Stop Watch Successes/ attempts

PKI Logon 1000 Local

Services Time

39

Table 6 (continued)

______________________________________________________________________

Measurement


103.3.6 Problem Continuous User Calls Completed Closed

Resolution / Total Incidents

Government

Applications

103.7.1 RAS Every 5 N/A Total RAS hours/

Service Minutes 1260 minutes x

Availability days in month

103.7.2 RAS 1 Hour N/A Successes/ attempts

Client 7 days/week

Responsiveness

103.8 Blackberry Continuous Automated Available Hours /

Services User Calls Total Hours

104.1.1 Speed to Continuous User Calls Total Seconds last prompt

Answer Help / Number calls answered

Desk Calls

104.1.2 Ave. Email / Continuous User Calls Total Response /

Voice mail Emails Total Tickets

Response

40

Table 6 (continued)

______________________________________________________________________

Measurement


104.2 Call Continuous User Calls Abandoned Calls /

Abandonment Offered Calls

Rate

104.3 First Call Continuous User Resolved Tickets /

Resolution Reports Closed Tickets

105 MAC Continuous N/A Varies

106.1 Security Per NMCI Varies Event Detections /

Event Reportable Events

Detection Event

106.2 Security Per NMCI Time of Event Detections /

Event Reportable Detection Events

Reporting Event

106.3 Security Per NMCI Logs Event Detections /

Event Reportable Reports Events

Response Event

106.4 Config. N/A Varies Properly Configured /

Management Total managed

107.1 Availability Every 5 Automated Varies

41

Table 6 (continued)

______________________________________________________________________

Measurement


Minutes

107.2 Latency / Every Automated Varies

Packet Loss Minute

107.3 Voice and Every 5 Automated Successes/ attempts

Video QOS Minutes

________________________________________________________________

Variables

There are many variables in this study. Table 7 (p.43) displays the

independent and dependant variables in this study. The independent variables

are servicing Network Operations Center (NOC), server farm, site size, service,

area, servicing region, and site group. The dependent variables are SLA

observations 101, 102, 103.1.1, 103.1.2, 103.1.3, 103.1.4,103.2, 103.3.1,

103.3.2, 103.3.4, 103.3.5, 103.3.6, 103.7.1, 103.7.2, 103.8, 104.1.1,104.1.2,

104.2, 104.3, 105, 106.1, 106.2, 106.3, 106.4, 107.1, 107.2, and 107.3.

42

Table 7

Independent and Dependent Variables

____________________________________________________________

Independent Variable (factors) Dependent Variables (SLA observations)

Servicing NOC 101, 102, 103.1.1, 103.1.2, 103.1.3, 103.1.4,

103.2, 103.3.1, 103.3.2, 103.3.4, 103.3.5,

103.3.6, 103.7.1, 103.7.2, 103.8, 104.1.1,

104.1.2, 104.2, 104.3, 105, 106.1, 106.2,

106.3, 106.4, 107.1, 107.2, 107.3

Servicing Server Farm 101, 102, 103.1.1, 103.1.2, 103.1.3, 103.1.4,

103.2, 103.3.1, 103.3.2, 103.3.4, 103.3.5,

103.3.6, 103.7.1, 103.7.2, 103.8, 104.1.1,

104.1.2, 104.2, 104.3, 105, 106.1, 106.2,

106.3, 106.4, 107.1, 107.2, 107.3

Customer Site Size 101, 102, 103.1.1, 103.1.2, 103.1.3, 103.1.4,

103.2, 103.3.1, 103.3.2, 103.3.4, 103.3.5,

103.3.6, 103.7.1, 103.7.2, 103.8, 104.1.1,

104.1.2, 104.2, 104.3, 105, 106.1, 106.2,

106.3, 106.4, 107.1, 107.2, 107.3

43

Table 7 (continued)

____________________________________________________________

Independent Variable (factors) Dependent Variables (SLA observations)

Service Area 101, 102, 103.1.1, 103.1.2, 103.1.3,

103.1.4, 103.2, 103.3.1, 103.3.2, 103.3.4,

103.3.5, 103.3.6, 103.7.1, 103.7.2, 103.8,

104.1.1, 104.1.2, 104.2, 104.3, 105, 106.1,

106.2, 106.3, 106.4, 107.1, 107.2, 107.3

Servicing Region 101, 102, 103.1.1, 103.1.2, 103.1.3, 103.1.4,

103.2, 103.3.1, 103.3.2, 103.3.4, 103.3.5,

103.3.6, 103.7.1, 103.7.2, 103.8, 104.1.1,

104.1.2, 104.2, 104.3, 105, 106.1, 106.2,

106.3, 106.4, 107.1, 107.2, 107.3

Servicing Site Group 101, 102, 103.1.1, 103.1.2, 103.1.3, 103.1.4,

103.2, 103.3.1, 103.3.2, 103.3.4, 103.3.5,

103.3.6, 103.7.1, 103.7.2, 103.8, 104.1.1,

104.1.2, 104.2, 104.3, 105, 106.1, 106.2,

106.3, 106.4, 107.1, 107.2, 107.3

_______________________________________________________________

44

Data Analysis

The most appropriate type of analysis for this study was MANOVA,

coupled with a one-way ANOVA. These procedures involve testing the equality of

multiple means or different levels within each factor. The random variable for

each of the treatments or levels is the observed response. In this case, the

observed response was the service level score. The design used a single

observation per response. The design had a total of six factors and up to twenty-

seven responses per factor (depending on data availability). Table 8 displays all

factors and associated treatments. The treatments were coded in the original

form of the raw data. Some treatments are abbreviations for cities, some are

lower case, and some are all upper case. The researcher chose to leave the

levels in their original forms to mitigate run errors. A translation of the coded

levels is provided in Appendix F.

Table 8 Factors and Treatments _______________________________________________________________________

Factor Type Levels Treatments

________________________________________________________________

Size Fixed 4 L, S, VSS, VSSD

Server Farm Fixed 25 BREM, CHLK, CHRL, CRAN, FALN,

JAXS, LEMR, LKHR, LTLC, MECH,

MILL, MUGU, NRFK, NWOR, OCEN,

45

Table 8 (continued)

_______________________________________________________________________


________________________________________________________________

ORLO, PAXR, PHIL, PRLH, PRTH,

SDNI, SDNS, SMTH, SPSC, WNYD

NOC Fixed 3 NRFK, PRLH, SDNI

Area Fixed 2 East, West

Region Fixed 7 Hawaii, MC East, MC West, North East,

North West, South East, South West

Group Fixed 64 ANACOSTIA, BANGOR, BREMERTON,

BRUNSWICK, CAMP PENDLETON,

CAMP SMITH, CARDEROCK,

CHARLESTON-SC, CHINA LAKE,

CORONA, CORPUS CHRISTI, CRANE,

CRYSTAL CITY, DAHLGREN,

FALLON, GREAT LAKES, GROTON,

GULFPORT/MERIDIAN, INDIAN HEAD,

JACKSONVILLE, KANEOHE BAY,

KINGS BAY, LAKEHURST, LEMOORE,

LITTLE CREEK, MAKALAPA,

MAYPORT, MCAS CHERRY POINT,

46

Table 8 (continued)

_______________________________________________________________________


________________________________________________________________

MCAS MIRAMAR, MCLB ALBANY,

MECHANICSBURG, MILLINGTON,

NAF WASHINGTON, NAS JRB FORT

WORTH, NAVAL BASE SAN DIEGO,

NAVY ANNEX, NEBRASKA AVE.,

NEWPORT, NO SITEGROUP, NORTH

ISLAND, NS NORFOLK, NS POINT

LOMA, NSA NORFOLK, NSY

NORFOLK, OLD TOWN, ORLANDO,

PANAMA CITY, PATUXENT RIVER,

PEARL HARBOR, PENSACOLA,

PENTAGON, PHILADELPHIA, POINT

MUGU, PORT HUENEME,

PORTSMOUTH-NH SPYD, REMOTE

MS/NEW ORLEANS, REMOTE MW/

NE, REMOTE NC/ NW/ OKLAHOMA,

REMOTE S/ SW, REMOTE SE,

VIRGINIA BEACH, WHIDBEY ISLAND,

47

Table 8 (continued)

_______________________________________________________________________


________________________________________________________________

WNY, YORKTOWN

Linear Model

The equation used to describe the observations and hypotheses relating

to the experiment is described below (Montgomery, 2001, pp. 65-66):

yĳ = µ + τі + εĳ i = 1, 2 ,…., a

j = 1, 2 ,…., n

For this effects model, yĳ represents the ijth observation, µ is the mean

common to all treatments, τі is the ith treatment effect, and εĳ is the random

error. In order to validate conclusions, the model’s residuals were checked for

normality. Appendix D shows a sample residual plot.

All hypothesis tests (including Tukey’s pairwise) used α = 0.05, which

corresponds to the 95% confidence level. Because the model is unbalanced,

Tukey’s test at the 95% confidence level is considered conservative, which

means that the actual confidence interval is actually greater than (1- α).

48

Appendices A and B provide analyses summaries; they each detail the

particulars associated with each run and track the results from ANOVA,

MANOVA, and any associated post hoc tests. On the basis of those results, both

conclusions and recommendation were made.

Personnel

No additional personnel were needed or required to execute this research

project. The next subsections provide background information regarding the

qualifications of the researcher as well as the credentials of the committee

members.

Qualifications of the Researcher

The researcher has over 16 years telecommunications experience with

the Navy in both civilian and military capacities. For the last five years, he has

developed excellent project management and research skills, working for the

regional plans and requirements department (N5) at the Naval Computer and

Telecommunications Area Master Station Atlantic (NCTAMS LANT). These

skills, coupled with his academic background, enabled him to successfully

complete the project within the project timeline.

Committee Members

The director and overall person in charge of this research was Dr. Tracy

Tillman. Dr. Tillman is the program director for the Master of Science in

49

Engineering Management at Eastern Michigan University. He is a full-time

professor, a Certified Manufacturing Engineer (CMfgE), a Certified Enterprise

Integrator (CEI), and a Certified Engineering Manager (CEM) (Eastern Michigan

University, 2005a).

The first reader was Robert E. Chapman. Dr. Chapman is a full time

professor in the Master of Science in Quality program at Eastern Michigan

University. He teaches courses in advanced SPC, designed experiments (DOE),

reliability engineering, and multivariate data analysis. Dr. Chapman has

consulted in the quality field for almost 20 years and has published in the journals

of Macromolecules, Biopolymers, and Quality Engineering (Eastern Michigan

University, 2005b).

The second reader, Hiral Shah, is an EMU Engineering Management

graduate and assistant program coordinator for the Master of Science in

Engineering Management at Eastern Michigan University. Additionally, she is a

doctoral student working on her Ph.D. in Technology at EMU.

Required Resources

A small budget, one personal computer equipped with a printer, and one

registered full version copy of Minitab® Release 14 were necessary for

undertaking and completing this study. The expenditures for this study are

displayed in Table 9.

50

Table 9 Allotted Budget _______________________________________________________________

Item Vendor Anticipated Cost Actual Cost

_______________________________________________________________

Analysis Software Minitab $120.00 $100.00

Paper & Envelopes Various 100.00 112.00

Postage Post Office 50.00 25.00

Total Cost $270.00 $237.00

Timeline The timeline for the study is provided in Figure 2. The following milestones

are provided: topic approved, secure committee members, proposal defense, first

rough draft completed, final defense, and final submission to the graduate

school.

51

Fig

ure

2.

Ga

ntt

cha

rt f

or

thesis

com

ple

tion

Fig

ure

2.

Ga

ntt

cha

rt f

or

thesis

com

ple

tion

.

52

Summary

The information gained from this research project, which is based on the

results and conclusions drawn from one-way ANOVA and MANOVA, will provide

Navy managers insight on where to focus efforts to improve network quality.

53

CHAPTER 4: PRESENTATION AND ANALYSIS OF DATA

Introduction

This chapter will provide information regarding the presentation of the

precollected, existing data collected as per the NMCI contract. Additionally, an

analysis of the data will be described on the basis of the methodology described

in chapter 3.

Data Presentation

The data used in this study leveraged existing, precollected data. The

actual spreadsheet presenting the data is named 20050930_EOM_Sep2005

FINAL. Site-specific (the actual location) information is sensitive and may not be

posted or presented in a means accessible to the general public. All site-specific

information was discarded prior to analysis. Only the factors and observations

described in chapters 1, 2, and 3 remained during the analysis.

Table 10 displays a sample of the raw data for SLA 101 presented to this

researcher.

54

Table 10 Sample Data Presented for SLA101

Note. Actual values for the specific sites have been omitted in this sample. A green position

indicates that the site passed the SLA requirement based on criteria in the contract. A red

position indicates failure to meet the required level.

55

Data Analysis

As previously discussed in chapter 3, the data were analyzed using

MANOVA, ANOVA, and Tukey’s pairwise test. The analysis results for all factors

and observations were recorded and are included in Appendix A.

Table 11 displays an abridged summary of all significant factors and levels.

Table 11 Significant Factors and Levels

SLA Factor Level

Significant? Disparate Level(s)

S101.B1 Group Yes MILLINGTON

S101.B3 Server Farm Yes LTLC

S103.1.3 NOC No N/A

S103.1.3 Region Yes NORTHEAST, NORTHWEST

S103.1.3 Group Yes

MCAS BEAUFORT, POINT MUGU

S103.3.1 NOC No N/A

S103.3.1 Region Yes HAWAII

S103.3.1 Region Yes NORTHEAST, NORTHWEST

S103.3.1 Group Yes

CHARLESTON, MAKALAPA, BEAUFORT, PARRIS ISLAND, PEARL HARBOR

S103.3.2 Site Size Yes L

S103.3.2 Region No N/A

S103.3.2 Group No N/A

S103.3.2 Server Farm Yes NWOR

S103.4 NOC No N/A

S103.4 Region No N/A

S103.4 Group Yes MAKALAPA

S103.6 B1 Group No N/A

S103.6 B3 Region Yes USMC - EAST

S103.6 B3 Group Yes

MCAS CHERRY POINT, MILLINGTON

56

Table 11 (continued)

SLA Factor Level

significant? Disparate

level(s)

S105.A NOC Yes PRLH

S105.A Region Yes HAWAII

S105.B NOC Yes PRLH

S107.2.l3 NOC No N/A

S107.2.l3 Area No N/A

S107.2.l3 Server Farm No N/A

S107.2.PL1 Group No N/A

S107.3.B Site Size No N/A

S107.3.B NOC No N/A

S107.3.B Group No N/A

S107.3.B Server Farm No N/A

From the table, it can be seen that not all significant factors have

significant levels. Also, if a level is recorded as disparate, it can be viewed as

either superior or inferior in performance as compared to others in its level.

Appendix B provides the unabridged summary for all post hoc Tukey tests. The

significant level is either marked as inferior or superior with regard to the level’s

relationship to others within the factor.

Significant ANOVA and MANOVA Results

Tables 12 through 38 provide either ANOVA or MANOVA Minitab output

results for each factor found to be significant (P value ≤ .05).

57

Table 12 ANOVA Results for S101.B1 (Site Group)

One-way ANOVA: T101.B1 versus Site Group Source DF SS MS F P

Site Group 64 3.7090 0.0580 1.42 0.031

Error 253 10.3372 0.0409

Total 317 14.0461

S = 0.2021 R-Sq = 26.41% R-Sq(adj) = 7.79%

According to the data in Table 12, Site Group is significant at the 95%

confidence level and accounts for 7.79% of the variability within this SLA.

Table 13 ANOVA Results for S101.B3 (Server Farm) One-way ANOVA: T101.B3 versus Server Farm Source DF SS MS F P

Server Farm 24 0.17116 0.00713 1.70 0.023

Error 293 1.22694 0.00419

Total 317 1.39810

S = 0.06471 R-Sq = 12.24% R-Sq(adj) = 5.05%

According to the data in Table 13, Server Farm is significant at the 95%


58

Table 14 MANOVA Results for S103.1.3 (NOC)

General Linear Model: T103.1.3 versus Site Size, NOC, Area MANOVA for Site Size

s = 1 m = 0.0 n = 177.5

Test DF

Criterion Statistic F Num Denom P

Wilks' 0.99576 0.760 2 357 0.469

Lawley-Hotelling 0.00426 0.760 2 357 0.469

Pillai's 0.00424 0.760 2 357 0.469

Roy's 0.00426

MANOVA for NOC

s = 1 m = 0.5 n = 177.5

Test DF


Wilks' 0.94610 6.780 3 357 0.000


Pillai's 0.05390 6.780 3 357 0.000

Roy's 0.05697

MANOVA for Area

s = 1 m = -0.5 n = 177.5

Test DF


Wilks' 0.99534 1.670 1 357 0.197


Pillai's 0.00466 1.670 1 357 0.197

Roy's 0.00468

According to the data in Table 14, only the factor NOC is significant at the

95% confidence level. The ANOVA method was used to calculate the R² values

in Appendix A. NOC accounts for 4.28% of the variability within this SLA.

59

Table 15 ANOVA Results for S103.1.3 (Region) One-way ANOVA: T103.1.3 versus Region Source DF SS MS F P

Region 8 0.0058939 0.0007367 7.51 0.000

Error 355 0.0348463 0.0000982

Total 363 0.0407401

S = 0.009907 R-Sq = 14.47% R-Sq(adj) = 12.54%

According to the data in Table 15, Region is significant at the 95%


Table 16 ANOVA Results for S103.1.3 (Site Group) One-way ANOVA: T103.1.3 versus Site Group Source DF SS MS F P

Site Group 65 0.0200793 0.0003089 4.46 0.000

Error 298 0.0206608 0.0000693

Total 363 0.0407401

S = 0.008327 R-Sq = 49.29% R-Sq(adj) = 38.22%



60

Table 17 MANOVA Results for S103.3.1 (NOC) General Linear Model: T103.3.1 versus Site Size, NOC, Area MANOVA for Site Size

s = 1 m = 0.0 n = 177.5

Test DF


Wilks' 0.99858 0.254 2 357 0.776


Pillai's 0.00142 0.254 2 357 0.776

Roy's 0.00142

MANOVA for NOC

s = 1 m = 0.5 n = 177.5

Test DF


Wilks' 0.97806 2.670 3 357 0.047


Pillai's 0.02194 2.670 3 357 0.047

Roy's 0.02244

MANOVA for Area

s = 1 m = -0.5 n = 177.5

Test DF


Wilks' 0.99512 1.750 1 357 0.187


Pillai's 0.00488 1.750 1 357 0.187

Roy's 0.00490

According to the data in Table 17, only NOC is significant at the 95%

confidence level. The ANOVA method was used to calculate the R² values in

Appendix A. NOC accounts for 1.94% of the variability within this SLA.

61

Table 18 ANOVA Results for S103.3.1 (Region) One-way ANOVA: T103.3.1 versus Region Source DF SS MS F P

Region 8 0.135191 0.016899 70.71 0.000

Error 715 0.170867 0.000239

Total 723 0.306058

S = 0.01546 R-Sq = 44.17% R-Sq(adj) = 43.55%



Table 19 ANOVA Results for S103.3.1 (Site Group) One-way ANOVA: T103.3.1 versus Site Group Source DF SS MS F P

Site Group 65 0.203794 0.003135 20.17 0.000

Error 658 0.102264 0.000155

Total 723 0.306058

S = 0.01247 R-Sq = 66.59% R-Sq(adj) = 63.29%



62

Table 20 MANOVA Results for S103.3.2 (Site Size) General Linear Model: S103.3.2 versus Site Size, NOC, Area MANOVA for Site Size

s = 1 m = -0.5 n = 124.5

Test DF


Wilks' 0.58432 178.557 1 251 0.000


Pillai's 0.41568 178.557 1 251 0.000

Roy's 0.71138

MANOVA for NOC

s = 1 m = 0.0 n = 124.5

Test DF


Wilks' 0.98280 2.197 2 251 0.113


Pillai's 0.01720 2.197 2 251 0.113

Roy's 0.01751

MANOVA for Area

s = 1 m = -0.5 n = 124.5

Test DF


Wilks' 0.98768 3.131 1 251 0.078


Pillai's 0.01232 3.131 1 251 0.078

Roy's 0.01247

According to the data in Table 20, only Site Size is significant at the 95%

confidence level. The ANOVA method was used to report the R² values in

Appendix A. Site Size accounts for 40.67% of the variability within this SLA.

63

Table 21 ANOVA Results for S103.3.2 (Region) One-way ANOVA: S103.3.2 versus Region Source DF SS MS F P

Region 8 357.60 44.70 7.71 0.000

Error 247 1432.83 5.80

Total 255 1790.42

S = 2.409 R-Sq = 19.97% R-Sq(adj) = 17.38%



Table 22 ANOVA Results for S103.3.2 (Site Group) One-way ANOVA: S103.3.2 versus Site Group Source DF SS MS F P

Site Group 62 897.90 14.48 3.13 0.000

Error 193 892.52 4.62

Total 255 1790.42

S = 2.150 R-Sq = 50.15% R-Sq(adj) = 34.14%



64

Table 23 ANOVA Results for S103.3.2 (Server Farm) One-way ANOVA: S103.3.2 versus Server Farm Source DF SS MS F P

Server Farm 24 760.98 31.71 7.11 0.000

Error 231 1029.45 4.46

Total 255 1790.42

S = 2.111 R-Sq = 42.50% R-Sq(adj) = 36.53%



Table 24 MANOVA Results for S103.4 (NOC) General Linear Model: T103.4 versus Site Size, NOC, Area MANOVA for Site Size

s = 1 m = 0.0 n = 177.5

Test DF


Wilks' 0.99682 0.569 2 357 0.566


Pillai's 0.00318 0.569 2 357 0.566

Roy's 0.00319

MANOVA for NOC

s = 1 m = 0.5 n = 177.5

Test DF


Wilks' 0.91321 11.310 3 357 0.000


Pillai's 0.08679 11.310 3 357 0.000

Roy's 0.09504

65


MANOVA for Area

s = 1 m = -0.5 n = 177.5

Test DF


Wilks' 0.99985 0.054 1 357 0.816


Pillai's 0.00015 0.054 1 357 0.816

Roy's 0.00015




Table 25 ANOVA Results for S103.4 (Region) One-way ANOVA: T103.4 versus Region Source DF SS MS F P

Region 8 0.0024942 0.0003118 20.43 0.000

Error 355 0.0054185 0.0000153

Total 363 0.0079127

S = 0.003907 R-Sq = 31.52% R-Sq(adj) = 29.98%



66

Table 26 ANOVA Results for S103.4 (Site Group) One-way ANOVA: T103.4 versus Site Group Source DF SS MS F P

Site Group 65 0.0033367 0.0000513 3.34 0.000

Error 298 0.0045760 0.0000154

Total 363 0.0079127

S = 0.003919 R-Sq = 42.17% R-Sq(adj) = 29.55%



Table 27 ANOVA Results for S103.6.B1 (Site Group) One-way ANOVA: T103.6.B1 versus Site Group Source DF SS MS F P

Site Group 62 2.8655 0.0462 1.47 0.047

Error 90 2.8269 0.0314

Total 152 5.6924

S = 0.1772 R-Sq = 50.34% R-Sq(adj) = 16.13%



67

Table 28 ANOVA Results for S103.6.B3 (Region) One-way ANOVA: T103.6.B3 versus Region Source DF SS MS F P

Region 8 0.09813 0.01227 2.53 0.013

Error 144 0.69783 0.00485

Total 152 0.79596

S = 0.06961 R-Sq = 12.33% R-Sq(adj) = 7.46%



Table 29 ANOVA Results for S103.6.B3 (Site Group) One-way ANOVA: T103.6.B3 versus Site Group Source DF SS MS F P

Site Group 62 0.58122 0.00937 3.93 0.000

Error 90 0.21475 0.00239

Total 152 0.79596

S = 0.04885 R-Sq = 73.02% R-Sq(adj) = 54.43%



68

Table 30 MANOVA Results for S105.A (NOC) General Linear Model: T105.A versus Site Size, NOC, Area MANOVA for Site Size

s = 1 m = -0.5 n = 152.0

Test DF


Wilks' 0.99925 0.230 1 306 0.632


Pillai's 0.00075 0.230 1 306 0.632

Roy's 0.00075

MANOVA for NOC

s = 1 m = 0.0 n = 152.0

Test DF


Wilks' 0.93114 11.315 2 306 0.000


Pillai's 0.06886 11.315 2 306 0.000

Roy's 0.07395

MANOVA for Area

s = 1 m = -0.5 n = 152.0

Test DF


Wilks' 0.99717 0.868 1 306 0.352


Pillai's 0.00283 0.868 1 306 0.352

Roy's 0.00284




69

Table 31 ANOVA Results for S105.A (Region) One-way ANOVA: T105.A versus Region Source DF SS MS F P

Region 8 1.3715 0.1714 2.35 0.018

Error 302 22.0628 0.0731

Total 310 23.4343

S = 0.2703 R-Sq = 5.85% R-Sq(adj) = 3.36%



Table 32 MANOVA Results for S105.B (NOC) General Linear Model: T105.B versus Site Size, NOC, Area MANOVA for Site Size

s = 1 m = -0.5 n = 152.0

Test DF


Wilks' 0.99115 2.733 1 306 0.099


Pillai's 0.00885 2.733 1 306 0.099

Roy's 0.00893

MANOVA for NOC

s = 1 m = 0.0 n = 152.0

Test DF


Wilks' 0.96375 5.755 2 306 0.004


Pillai's 0.03625 5.755 2 306 0.004

Roy's 0.03761

70


MANOVA for Area

s = 1 m = -0.5 n = 152.0

Test DF


Wilks' 0.99992 0.025 1 306 0.876


Pillai's 0.00008 0.025 1 306 0.876

Roy's 0.00008




Table 33 MANOVA Results for S107.2.I3 (NOC and Area) General Linear Model: T107.2.L3 versus Site Size, NOC, Area MANOVA for Site Size

s = 1 m = -0.5 n = 79.5

Test DF


Wilks' 0.99194 1.308 1 161 0.254


Pillai's 0.00806 1.308 1 161 0.254

Roy's 0.00812

MANOVA for NOC

s = 1 m = 0.0 n = 79.5

Test DF


Wilks' 0.77996 22.710 2 161 0.000


Pillai's 0.22004 22.710 2 161 0.000

Roy's 0.28211

71


MANOVA for Area

s = 1 m = -0.5 n = 79.5

Test DF


Wilks' 0.94315 9.705 1 161 0.002


Pillai's 0.05685 9.705 1 161 0.002

Roy's 0.06028

According to the data in Table 33, both NOC and Area are significant at

the 95% confidence level. The ANOVA method was used to report the R² values

in Appendix A. NOC accounts for 15.84% of the variability within this SLA. Area

accounts for 0.00% of the variability within this SLA.

Table 34 MANOVA Results for S107.2.I3 (Server Farm) One-way ANOVA: T107.2.L3 versus Server Farm Source DF SS MS F P

Server Farm 20 1.0178 0.0509 1.73 0.035

Error 145 4.2723 0.0295

Total 165 5.2901

S = 0.1717 R-Sq = 19.24% R-Sq(adj) = 8.10%



72

Table 35 ANOVA Results for S107.2.PL1 (Site Group) One-way ANOVA: T107.2.PL1 versus Site Group Source DF SS MS F P

Site Group 65 0.15768 0.00243 2.16 0.000

Error 237 0.26657 0.00112

Total 302 0.42425

S = 0.03354 R-Sq = 37.17% R-Sq(adj) = 19.93%



Table 36 MANOVA Results for S107.3.B (Site Size and NOC) General Linear Model: T107.3.B versus Site Size, NOC, Area MANOVA for Site Size

s = 1 m = -0.5 n = 52.5

Test DF


Wilks' 0.63804 60.700 1 107 0.000


Pillai's 0.36196 60.700 1 107 0.000

Roy's 0.56729

MANOVA for NOC

s = 1 m = 0.0 n = 52.5

Test DF


Wilks' 0.94520 3.102 2 107 0.049


Pillai's 0.05480 3.102 2 107 0.049

Roy's 0.05798

73


MANOVA for Area

s = 1 m = -0.5 n = 52.5

Test DF


Wilks' 0.97416 2.838 1 107 0.095


Pillai's 0.02584 2.838 1 107 0.095

Roy's 0.02653

According to the data in Table 36, Site Size and NOC are significant at the

95% confidence level. The ANOVA method was used to report the R² values in

Appendix A. Site Size accounts for 34.16% of the variability within this SLA.

NOC accounts for 2.19% of the variability within this SLA.

Table 37 ANOVA Results for S107.3.B (Site Group) One-way ANOVA: T107.3.B versus Site Group Source DF SS MS F P

Site Group 20 5.3572 0.2679 6.71 0.000

Error 91 3.6322 0.0399

Total 111 8.9894

S = 0.1998 R-Sq = 59.59% R-Sq(adj) = 50.71%



74

Table 38 ANOVA Results for S107.3.B (Server Farm) One-way ANOVA: T107.3.B versus Server Farm Source DF SS MS F P

Server Farm 16 3.1477 0.1967 3.20 0.000

Error 95 5.8417 0.0615

Total 111 8.9894

S = 0.2480 R-Sq = 35.02% R-Sq(adj) = 24.07%



Each of the six factors is significant for one or more SLAs. Factor NOC

was found to be significant seven times. Factor Server Farm was found to be

significant four times. Factor Site Size was found to be significant three times.

Factor Area was found to be significant only one time. Factor Region was found

to be significant six times. And finally, factor Site Group was found to be

significant nine times.

Tukey’s Test for Significant Factors

As mentioned in chapter 3, Tukey’s test would be run if a factor was found

to be significant. Table 39 summarizes significant factors as well as levels found

to be significant by Tukey’s test.

75

Table 39 Tukey’s Results for Significant Factors

SLA Factor Disparate Level(s)

S101.B1 Group MILLINGTON

S101.B3 Server Farm LTLC

S103.1.3 Region NORTHEAST, NORTHWEST

S103.1.3 Group

MCAS BEAUFORT, POINT MUGU

S103.3.1 Region HAWAII

S103.3.1 Region NORTHEAST, NORTHWEST

S103.3.1 Group

CHARLESTON, MAKALAPA, BEAUFORT, PARRIS ISLAND, PEARL HARBOR

S103.3.2 Site Size L

S103.3.2 Server Farm NWOR

S103.4 Group MAKALAPA

S103.6 B3 Region USMC - EAST

S103.6 B3 Group

MCAS CHERRY POINT, MILLINGTON

S105.A NOC PRLH

S105.A Region HAWAII

S105.B NOC PRLH

76

Figures 3 through 23 in Appendix G display Tukey’s test results in which a

level was found disparate. As discussed in Chapter 3, a level is found disparate

if other levels fall outside of its confidence interval when subtracted (zero not

included on the interval following a pairwise subtraction).

According to the data in Figure 3, Site Group MILLINGTON is statistically

inferior to at least eight other groups. According to the data in Figure 4, Server

Farm LTLC is statistically inferior to at least eleven other server farms.

According to the data in Figure 5, Regions NORTHEAST and NORTHWEST are

statistically superior to at least six other regions. According to the data in Figure

6, MCAS BEAUFORT is statistically inferior to at least sixteen other groups.

According to the data in Figure 7, POINT MUGU is statistically inferior to at least

eight other groups. According to the data in Figure 8, region HAWAII is

statistically inferior to at least eight other groups. According to the data in Figure

9, regions Northeast and Northwest are statistically superior to at least five other

regions. According to the data in Figure 10, Site Group CHARLESTON-SC is

statistically inferior to at least twenty-two other groups. According to the data in

Figure 11, Site Group MAKALAPA is statistically inferior to at least twenty-five

other groups. According to the data in Figure 12, Site Group MCAS BEAUFORT

is statistically inferior to at least twenty-five other groups. According to the data

in Figure 13, Site Group PARRIS ISLAND is statistically inferior to at least seven

other groups. According to the data in Figure 14, Site Group PEARL HARBOR is

statistically inferior to at least seven other groups. According to the data in Figure

15, Site Size L is statistically superior to Site Size S. According to the data in

77

Figure 16, Server Farm NWOR is statistically inferior to Site Size S. According to

the data in Figure 17, Site Group MAKALAPA is statistically inferior to at least

twenty-four other groups. According to the data in Figure 18, region USMC –

EAST is statistically superior to at least seven other regions. According to the

data in Figure 19, Site Group MCAS CHERRY POINT is statistically inferior to at

least thirty-two other groups. According to the data in Figure 20, Site Group

MILLINGTON is statistically inferior to at least thirty-one other groups. According

to the data in Figure 21, NOC PRLH is statistically inferior to at least two other

NOCs. According to the data in Figure 22, region HAWAII is statistically inferior

to at least three other regions. According to the data in Figure 23, NOC PRLH is

statistically inferior to at least two other NOCs.

Not all significant factors had significant levels. Figures 3 through 23 in

Appendix G display only levels found to be disparate. Significant factors

outnumbered disparate levels 2 to 1.

Equal Variance Test

For each factor that was found to be statistically significant, a test of equal

variance among the levels within the factor was conducted. For factors with only

two levels, an F-test with a P value ≤ .05 indicates variance is not equal between

the levels. For factors with more than three levels, Bartlett’s test statistic was

used. Similarly to the F-test, a Bartlett P value ≤ .05 indicates that variance is not

equal between at least two of the levels. Figures 24 through 52 in Appendix H

display the results of equal variance tests for each factor.

78

According to Bartlett’s test in Figure 24, at least one level does not have

the same variance as others in the group because P ≤ .05. For this factor, there

appear to be seven levels that do not have equal variance. Level MILLINGTON

(the significant level for this factor) has by far the most variance of the group.

CAMP PENDELTON, JACKSONVILLE, MCAS BEAUFORT, MCAS MIRAMAR,

NEBRASKA AVE, and YORKTON all have data values that vary more than those

of the other groups.


the same variance as others in the group because P ≤ .05. For this factor, there

appear to be five levels that do not have equal variance. Level LTLC (the

significant level for this factor) has by far the most variance of the group. CHLK,

OCEN, PAXR, MCAS, and PRTH all have data values that vary more than those

of the other groups.

According to the F-Test in Figure 26, both levels have the same variance

because P ≤ .05. For this factor, no levels are significant, and NRFK and SDNI

have equal variance.


the same variance as others in the group because P ≤ .05. For this factor, all

levels do not have equal variance. Levels NORTHEAST and NORTHWEST (the

significant levels for this factor) appear to have the least variance in the group.

USMC-EAST and USMC WEST appear to have similar variance and vary more

than the other groups.

79


the same variance as others in the group because P ≤ .05. For this factor, three

levels appear not to have equal variance. Level MCAS BEAFORT (the

significant level for this factor) has minimal variance. Levels ANACOSTIA,

LEMOORE, and MCAS MIRAMAR all have data values that vary more than

those of the other groups.


the same variance as others in the group because P ≤ .05. For this factor, no

levels are significant, and NRFK and SDNI appear to have equal variance. Data

values for level PRLH seem to vary the most.



levels appear not to have equal variance. Levels HAWAII, SOUTHEAST,

TIDEWATER, USMC-EAST, and USMC WEST have data values that vary more

than those of the other groups. Levels HAWAII, NORTHEAST, and

NORTHWEST are significant for this factor.


the same variance as others in the group because P ≤ .05. For this factor, four

levels appear not to have equal variance. Levels CHARLESTON, MAKALAPA,

MCAS BEAFORT, PARRIS ISLAND, and PEARL HARBOR are the significant

levels for this factor. Levels ANACOSTIA, CAMP PENDLETON, LEMOORE, and

MCAS MIRAMAR all have data values that vary more than those of the other

groups.

80

According to the F-Test in Figure 32, at least one level does not have the

same variance as others in the group because P ≤ .05. For this factor, both levels

appear not to have equal variance. Levels L is the significant level for this factor.



levels appear not to have equal variance. Levels HAWAII, USMC-EAST, and

USMC WEST have data values that vary more than those of the other groups.

No levels are significant for this factor.


the same variance as others in the group because P ≤ .05. For this factor, at

least eight levels appear not to have equal variance. Levels CHARLESTON SC,

JACKSONVILLE, MCAS MIRAMAR, MILLINGTON, NS NORFOLK, ORLANDO,

POINT MUGU, and WNY have data values that vary more than those of the

other groups. No levels are significant for this factor.



least two levels appear not to have equal variance. Levels OCEN and PRTH

have data values that vary more than those of the other groups. Level NWOR is

significant for this factor.


same variance as the other because P ≤ .05. For this factor, level PRLH appears

to vary more than level NRFK. No levels are significant for this factor.

81



three levels appear not to have equal variance. Levels HAWAII and

TIDEWATER have data values that vary more than those of the other groups. No

levels are significant for this factor.



least three levels appear not to have equal variance. Levels CAMP SMITH,

REMOTE SE, and VIRGINIA BEACH have data values that vary more than those

of the other groups. Level MAKALAPA is significant for this factor.

According to Bartlett’s test in Figure 39, all levels have the same variance

because P ≥ .05. For this factor, at least seven levels appear not to have equal

variance. Visual inspection of the data suggests that Bartlett’s test may be

incorrect. No levels are significant for this factor.



least two levels appear not to have equal variance. Levels SOUTHEAST and

USMC-EAST have data values that vary more than those of the other groups.

Level USMC-EAST is significant for this factor.



least four levels appear not to have equal variance. Levels CHARLESTON SC,

MCAS CHERRY POINT, NS NORFOLK, and PATUXANT RIVER have data

82

values that vary more than those of the other groups. Levels MCAS CHERRY

POINT and MILLINGTON are significant for this factor.



levels appear not to have equal variance. Level PRLH has data values that vary

more than those of the others in the group. Level PRLH is significant for this

factor.



levels appear not to have equal variance. Levels HAWAII and USMC – EAST

have data values that vary more than those of the others in the group. Level

HAWAII is significant for this factor.





factor.





factor.

83


same variance as the other because P ≤ .05. For this factor, level West appears

to vary more than level East. No levels are significant for this factor.


the same variance as others in the group because P ≤ .05. For this factor, four

levels appear not to have equal variance. Levels BREM, CHLK, MILL, and

OCEN have data values that vary more than those of the others in the group.

Level BREM is significant for this factor.


the same variance as others in the group because P ≤ .05. For this factor, seven

levels appear not to have equal variance. Levels ANACOSTIA, CAMP

PENDLETON, JACKSONVILLE, MCAS CHERRY POINT, MCAS MIRAMAR,

MILLINGTON, and NEBRASKA AVE have data values that vary more than those

of the others in the group. Levels ANACOSTIA and MCAS CHERRY POINT are



same variance as the other because P ≤ .05. For this factor, level L appears to

vary more than level S. Both levels are significant for this factor.

According to Bartlett’s test in Figure 50, all levels have the same variance

because P ≥ .05. For this factor, all levels appear not to have equal variance.

Level PRLH has seems to have data values that vary more than those of the

others in the group. No levels are significant for this factor.

84


the same variance as others in the group because P ≤ .05. For this factor, three

levels appear not to have equal variance. Levels GREAT LAKES, LOS

ANGELES, and WHIDBEY ISLAND have data values that vary more than those

of the others in the group. No levels are significant for this factor.


the same variance as others in the group because P ≤ .05. For this factor, two

levels appear not to have equal variance. Levels JAXS and SPSC have data

values that vary more than those of the others in the group. No levels are


Residuals Analysis

For each factor that was found to be statistically significant, residual plots

were plotted. The four in one plot contain the normal probability plots, residual

vs. fitted values, a histogram of the residuals, and residuals vs. the order of the

data. Figures 53 through 81 in Appendix I display the residual analysis results

for each factor.

According to the plots displayed in Figure 53, the normal probability plot is

mostly linear but curves at both ends. A few outliers are present. Some evidence

of nonnormality is present. The residual vs. fitted values appear without structure.

Observations do not tend to increase as the magnitude of observations

increases. The assumption of homogeneity does not seem violated. The

histogram of residuals seems left skewed, nonnormal, and with outliers. The

85

residuals vs. the order of data displays some correlation between residuals. The

independence assumption seems to be in violation.


mostly linear but curves at both ends. A few outliers are present. Some evidence

of nonnormality is present. The residual vs. fitted values appear with structure.

Observations do tend to increase as the magnitude of observations increase, so

the assumption of homogeneity seems violated. The histogram of residuals

seems symmetric and nonnormal and to contain outliers. The residuals vs. the

order of data display some correlation between residuals. The independence

assumption seems to be in violation.


far from linear. Although no outliers are present, some evidence of nonnormality

is present. The residual vs. fitted values appear without structure. Observations

do not tend to increase as the magnitude of observations increase. The

assumption of homogeneity does not seem violated. The histogram of residuals

seems to be symmetric and nonnormal and to contain outliers. The residuals vs.

the order of data display some correlation between residuals. The independence



far from linear. Although no outliers are present, some evidence of nonnormality

is present. The residual vs. fitted values appear without structure. Observations

do not tend to increase as the magnitude of observations increase. The

assumption of homogeneity does not seem violated. The histogram of residuals

86

seems to be symmetric and nonnormal and to contain outliers. The residuals vs.

the order of data display some correlation between residuals. The independence



somewhat linear. Few outliers are present, so little evidence of nonnormality is

present. The residual vs. fitted values appear with structure (horn shaped).

Observations tend slightly to increase as the magnitude of observations increase.

The assumption of homogeneity seems violated. The histogram of residuals

seems nonnormal. The residuals vs. the order of data display some correlation

between residuals. The independence assumption seems to be in violation.


mostly linear but curves at both ends. A few outliers are present. No evidence of

nonnormality is present. The residual vs. fitted values appear with structure.

Observations do tend to increase as the magnitude of observations increase, so

the assumption of homogeneity seems violated. The histogram of residuals

seems normal but with outliers. The residuals vs. the order of data display some

correlation between residuals. The independence assumption seems to be in

violation.


somewhat linear. No outliers are present, so little evidence of nonnormality is

present. The residual vs. fitted values appear without structure. Observations

tend not to increase as the magnitude of observations increase. The assumption

of homogeneity does not seem violated. The histogram of residuals seems

87

symmetric and nonnormal. The residuals vs. the order of data display some


violation.


somewhat linear. Few outliers are present, so little evidence of nonnormality is

present. The residual vs. fitted values appear with structure (horn shaped).

Observations tend to increase slightly as the magnitude of observations increase.

The assumption of homogeneity seems violated. The histogram of residuals

seem nonnormal and with outliers. The residuals vs. the order of data display

some correlation between residuals. The independence assumption seems to be

in violation.


mostly linear but curves at one end. No outliers are present. No evidence of

nonnormality is present. The residual vs. fitted values appear without structure.

Observations do not tend to increase as the magnitude of observations increase,

so the assumption of homogeneity does not seem violated. The histogram of

residuals seems right skewed. The residuals vs. the order of data display some


violation.

According to the plots displayed in Figure 62, the normal probability plots

is mostly linear but curves at both ends. No outliers are present. No evidence of

nonnormality is present. The residual vs. fitted values appear with structure (horn

shaped). Observations do not tend to increase as the magnitude of observations

88

increase. The assumption of homogeneity seems violated. The histogram of

residuals seems slightly bimodal. The residuals vs. the order of data display


in violation.


is mostly linear but curves at one end. No outliers are present. No evidence of




residuals seems nonnormal. The residuals vs. the order of data display some


violation.


is mostly linear but curves at both ends. Few outliers are present. No evidence of




residuals seems normal. The residuals vs. the order of data display some


violation.


far from linear. Three outliers are present; evidence of nonnormality is present.

The residual vs. fitted values appear without structure. Observations do not tend

89

to increase as the magnitude of observations increase. The assumption of

homogeneity does not seem violated. The histogram of residuals seems to be

symmetric and nonnormal and to contain outliers. The residuals vs. the order of

data display some correlation between residuals. The independence assumption

seems to be in violation.


far from linear. Five outliers are present; evidence of nonnormality is present.








far from linear. Four outliers are present; evidence of nonnormality is present.








somewhat linear. Few outliers are present; evidence of nonnormality is present.

90

The residual vs. fitted values appear with structure (horn shaped). Observations

do tend to slightly increase as the magnitude of observations increase. The

assumption of homogeneity seems violated. The histogram of residuals seems

to be symmetric and nonnormal and to contain outliers. The residuals vs. the

order of data display some correlation between residuals. The independence



far from linear. Five outliers are present; evidence of nonnormality is present.

The residual vs. fitted values appear without structure. Observations tend to

increase as the magnitude of observations increase. The assumption of

homogeneity seems violated. The histogram of residuals seems to be symmetric

and nonnormal and to contain outliers. The residuals vs. the order of data display


in violation.


far from linear. Two outliers are present; evidence of nonnormality is present.

The residual vs. fitted values appear with structure. Observations tend to





in violation.

91


somewhat linear. Many outliers are present; evidence of nonnormality is present.






in violation.















92


in violation.



The residual vs. fitted values appear with structure (horn shaped). Observations

tend to increase as the magnitude of observations increase. The assumption of




in violation.


far from linear. Few outliers are present; evidence of nonnormality is present.





data do not display correlation between residuals. The independence assumption

is not in violation.


not linear. Few outliers are present; evidence of nonnormality is present. The

residual vs. fitted values appear with structure (horn shaped). Observations tend



93



in violation.


linear. Few outliers are present; evidence of nonnormality is not present. The



homogeneity seems violated. The histogram of residuals seems mostly normal.

The residuals vs. the order of data display some correlation between residuals.

The independence assumption seems to be in violation.



residual vs. fitted values appear without structure. Observations do not tend to


homogeneity does not seem violated. The histogram of residuals seems mostly

normal. The residuals vs. the order of data display some correlation between

residuals. The independence assumption seems to be in violation.


mostly linear. One outlier is present; evidence of nonnormality is not present. The

residual vs. fitted values appear without structure. Observations do not tend to


homogeneity does not seem violated. The histogram of residuals seems mostly

94

normal. The residuals vs. the order of data display some correlation between

residuals. The independence assumption seems to be in violation.




to decrease as the magnitude of observations increase. The assumption of

homogeneity seems violated. The histogram of residuals seems normal. The

residuals vs. the order of data display some correlation between residuals. The



linear. No outliers are present; evidence of nonnormality is not present. The


to decrease as the magnitude of observations increase. The assumption of

homogeneity seems violated. The histogram of residuals seems normal. The

residuals vs. the order of data display some correlation between residuals. The


Overall, the plots displayed signs of nonnormal behavior (a poorly shaped

normal curve) at least half of the time. In addition to the many nonnormal curves,

the assumption of homogeneity was violated many times.

Box Plot Analysis

Box plots were created for each factor that was found to be statistically

significant. Box plots are used to examine characteristics of the sample

95

distribution. The plots contain, depending on data availability, each level’s

median, upper limits, lower limits, and outliers. The outliers are signified by an

asterisk. Figures 82 through 110 in Appendix J display the box plots for each

factor.

The plot in Figure 82 displays three extreme outliers: one in level

PHILIDELPHIA, one in level REMOTE MW/NE, and one in level REMOTE S/SW.

Level MILLINGTON, the significant level for this factor, is visibly disparate. Its

median value is much lower than those of the others. Level MILLINGTON has

more data variability than others in the group.

The plot in Figure 83 displays many outliers: four in level BREM, one in

level CHRL, one in level CRAN, four in level FALN, one in level JAXS, one in

level LKHR, two in level MECH, one in level, MUGU, two in level, NWOR, two in

level PHIL, two in level SDNI, one in level SDNS, and one in level WNYD. Level

LTLC, the significant level for this factor, is visibly disparate. Its median value is

much lower than those of the others. Level LTLC has more data variability than

others in the group.

The plot in Figure 84 displays three extreme outliers: two in level NRFK

one in level SDNI. There were no significant levels for this factor. Level SDNI has

more data variability than others in the group.

The plot in Figure 85 displays three extreme outliers: one in level

NORTHEAST, one in level NORTHWEST, and one in level SOUTHWEST.

Levels NORTHEAST and NORTHWEST, the significant levels for this factor, are

visibly disparate to at least five other levels. Their median values are higher than

96

those of SOUTHEAST, SOUTHWEST, TIDEWATER, USMC – EAST, and

USMC – WEST. Tukey’s test did not indicate levels HAWAII and NCR as being

significant because their 95% confidence interval is wider than those of levels

NORTHEAST and NORTHWEST.

The plot in Figure 86 displays four extreme outliers: one in level

CHARLESTON SC, one in level CORPUS CHRISTI, one in level REMOTE

SC/NW/OKLAHOMA, and one in level REMOTE S/SW. Levels MCAS

BEAUFORT and POINT MUGU, the significant levels for this factor, are visibly

disparate. Their median values are much lower than those of most others.

Tukey’s test did not indicate levels CHARLESTON SC and PARRIS ISLAND as

being significant because their 95% confidence interval is wider than those of

levels MCAS BEAUFORT and POINT MUGU.

The plot in Figure 87 displays one extreme outlier: one in level NRFK.

There were no significant levels for this factor. Level PRLH has more data

variability than others in the group. Level QUAN could be considered disparate,

but it was not found to be significant according to Tukey’s test.

The plot in Figure 88 displays seven extreme outliers: one in level

HAWAII, one in level NCR, three in level NORTHEAST, and two in level

NORTHWEST. Levels HAWAII, NORTHEAST, and NORTHWEST, the

significant levels for this factor, are visibly disparate. HAWAII’s median is much

lower than those of the others. NORTHEAST and NORTHWEST’s median

values are much higher than those of most others.

97

The plot in Figure 89 displays four extreme outliers: one in level

CHARLESTON SC, one in level CORPUS CHRISTI, one in level CRYSTAL

CITY, one in level REMOTE MW/NE, and two in level REMOTE NC/NW/

OKLAHOMA. Levels CHARLESTON SC, MAKALAPA, and MCAS BEAUFORT,

PARRIS ISLAND, and PEARL HARBOR, the significant levels for this factor, are

visibly disparate.

The plot in Figure 90 displays many outliers for each level: five in level L

and seven in level S. Level L, the significant level for this factor, is visibly

disparate. L’s median value is much lower than that of level S.

The plot in Figure 91 displays nine outliers: four in level HCR, three in

level NORTHEAST, one in level NORTHWEST, and one in level SOUTHWEST.

There were no significant levels for this factor.

The plot in Figure 92 displays five outliers: three in level REMOTE S/SW

and three in level REMOTE SE. There were no significant levels for this factor.

The plot in Figure 93 displays ten outliers: one in level CHRL, one in level

JAXS, one in level MECH, one in level NWOR, two in level PAXR, one in level

SDNI, one in level SDNS, one line level SPSC, and one in level WNYD. Level

NWOR is visibly disparate and considered significant for this factor. NWOR’s

median is much higher than those of others in the group.

The plot in Figure 94 displays only one extreme outlier in level NRFK.

There were no significant levels for this factor. PRLH appears to have the most

variability of data in the group.

98

The plot in Figure 95 displays three outliers: one in level HAWAII, one in

level SOUTHEAST, and one in level TIDEWATER. There were no significant

levels for this factor.

The plot in Figure 96 displays only one extreme outlier in level REMOTE

SE. MAKALAPA is the significant level for this factor. Levels KANEOHE BAY

and PEARL HARBOR were not found to be significant by Tukey’s test.

KANEOHE BAY and PEARL HARBOR’s 95% confidence interval was much

wider than MAKALAPA’s.

The plot in Figure 97 displays four outliers: one in level NAVAL BASE

SAN DIEGO, one in level REMOTE S/SW, and two in level REMOTE SE. There

were no significant levels for this factor.

The plot in Figure 98 displays four outliers: one in level NCR, one in level

SOUTHEAST, and two in level TIDEWATER. USMC-EAST is the significant level

for this factor. Not only is it visibly disparate, but it also displays the most data

variability.

The plot in Figure 99 displays one outlier. Levels MCAS CHERRY POINT

and MILLINGTON are the significant levels for this factor. MCAS CHERRY

POINT and MILLINGTON are visibly disparate. Their medians are much lower

than those of the others in the group. MCAS CHERRY POINT has the most data

variability of the group.

The plot in Figure 100 displays fourteen outliers: ten in level NRFK, one in

level PRLH, and three in level SDNI. PRLH is the significant level for this factor.

Not only is it visibly disparate, but it also displays the most data variability.

99

The plot in Figure 101 displays seventeen outliers: one in level HAWAII,

one in level NCR, six in level NORTHEAST, three in level NORTHWEST, four in

level SOUTHEAST, and two in level SOUTHWEST. HAWAII is the significant

level for this factor. HAWAII is visibly disparate.

The plot in Figure 102 displays many outliers: many in level NRFK, two in

level PRLH, and approximately five in level SDNI. PRLH is the significant level

for this factor. Not only is it visibly disparate, but it also displays the most data

variability.

The plot in Figure 103 displays eight outliers: three in level NRFK, one in

level PRLH, and four in level SDNI. PRLH is the significant level for this factor.

Not only is it visibly disparate, but it also displays the most data variability.

The plot in Figure 104 displays many outliers: three in level East and five

in level West. No levels are significant as visibly verifiable.

The plot in Figure 105 displays ten outliers: one in level BREM, one in

level CHLK, two in level FALN, two in level MECH, two in level NWOR, and two

in level SDNI. As visibly verifiable, level BREM is significant. Its median value is

much lower than those of the others. Level BREM also appears to have the most

data variability.

The plot in Figure 106 displays fourteen outliers: two in level CRYSTAL

CITY, five in level REMOTE MW/ NE, three in level REMOTE NC/NW/

OKLAHOMA, two in level REMOTE S/SW, and two in level REMOTE SE. Levels

ANACOSTIA and MCAS CHERRY POINT, the significant levels for this factor,

100

are visibly disparate. Both levels contain more data variability than those of the

others in the group.

The plot in Figure 107 displays three outliers in level S. Level L and S are

significant level for this factor. L’s median value is much higher than that of level

S, and S’s median value is much lower than that of level L.

The plot in Figure 108 displays four outliers: two in level NRFK and two in

level SDNI. As visibly verifiable, no levels are significant.

The plot in Figure 109 displays two outliers: one in level REMOTE

NC/NW/ OKLAHOMA and one in level REMOTE SE. As visibly verifiable, no

levels are significant.

The plot in Figure 110 displays only one outlier in level CHRL. As visibly

verifiable, no levels are significant.

In general, most of the significant factors have occurrences of outliers.

The presence of outliers tends to skew results and should be investigated. In

most cases, when a level was found to be significant according to Tukey’s test,

the box plot of the disparate level visibly displayed the significant level.

Summary

This chapter provided information regarding the presentation of the pre-

collected, existing data collected as per the NMCI contract. Additionally, the

results of the analysis of the data, as described in Chapter 3, were provided.

Specifically, the results of both ANOVA and MANOVA tests, the significant

101

results of Tukey’s test, and the results of equal variance, residuals, and box plots

were provided.

102

CHAPTER 5: RESULTS, CONCLUSIONS, AND RECOMMENDATIONS

Introduction

This chapter draws from the results and the findings discussed in chapter

4 to make statements regarding conclusions. And then, on the basis of these

conclusions, this chapter provides recommendations for future courses of action

to include follow-up research.

Results

The results of the analyses in Chapter 4 indicate that at least one of every

six factors is significant for either one or more SLAs. On the basis of the results

and interpretation of the analyses, the following statements can be made:

(a) Factor NOC is significant for seven SLAs or sub-SLAs.

(b) Factor Server Farm is significant for four SLAs or sub-SLAs.

(c) Factor Site Size is significant for three SLAs or sub-SLAs.

(d) Factor Area is significant for one SLA or sub-SLA.

(e) Factor Region is significant for six SLAs or sub-SLAs.

(f) Factor Site Group is significant for nine SLAs or sub-SLAs.

Regarding the significant levels for each factor found to be significant, not

all significant factors had significant levels. Of the 30 times a factor was found to

be significant, only 15 factors contained levels that were disparate. Ten levels

were found to be significant in only one SLA or sub-SLA. Nine levels were found

to be significant in two SLAs or sub-SLAs. Only one level was found to be

significant in three SLAs or sub-SLAs. Of all the significant levels, only three

103

were found to be superior in performance when compared to others in their

respective groups. Table 40 provides a summary of levels found to be disparate.

Table 40 Summary of Significant Levels ___________________________________________________________________

Level Relationship SLA(s) _________________

ANACOSTIA Inferior S107.2.PL1

BREM Inferior S107.2.L3

CHARLESTON Inferior S103.3.1

HAWAII Inferior S103.3.1, S105.A

L Superior S103.3.2, S107.3.B

LTLC Inferior S101.B3

MAKALAPA Inferior S103.3.1, S103.4,

MCAS CHERRY POINT Inferior S103.6.B3, S107.2.PL1

MCAS BEAUFORT Inferior S103.1.3, S103.3.1

MILLINGTON Inferior S101.B1, S103.6.B3

NORTHEAST Superior S103.1.3, S103.3.1

NORTHWEST Superior S103.1.3, S103.3.1

NWOR Inferior S103.3.2

PARRIS ISLAND Inferior S103.3.1

PEARL HARBOR Inferior S103.3.1

PLRH Inferior S105.A, S105.B, S107.2.L3

104


_____________________________________________________________

Level Relationship SLA(s) _________________

POINT MUGU Inferior S103.1.3

S Inferior S107.3.B3

USMC - EAST Inferior S103.6.B3

Conclusions

Table 1 displays a list of hypotheses. Table 41 summarizes the results

the hypotheses, followed by explanation.

Table 41

Test of Hypotheses Results

Factor Null (Ho) Alternative (H1)________Action________________

NOC No difference Significant difference Ho rejected, H1 accepted

Server farm No difference Significant difference Ho rejected, H1 accepted

Site size No difference Significant difference Ho rejected, H1 accepted

Area No difference Significant difference Ho rejected, H1 accepted

105


Test of Hypotheses Results

Factor Null (Ho) Alternative (H1)________Action________________

Region No difference Significant difference Ho rejected, H1 accepted

Site group No difference Significant difference Ho rejected, H1 accepted

________________________________________________________________

On the basis of the results and interpretations of the analyses, the

following statement can be made regarding the factor NOC: There is enough

evidence to reject the null hypothesis and accept the alternative hypothesis that a

significant difference exists for SLAs S103.1.3, S103.3.1, S103.4, S105.A,

S105.B, S107.2.I3, and S107.3.B. The following statement can be made

regarding the factor server farm: There is enough evidence to reject the null

hypothesis and accept the alternative hypothesis that a significant difference

exists for SLAs S101.B3 and S103.3.2. The following statement can be made

regarding the factor site size: There is enough evidence to reject the null


exists for SLAs S103.3.2 and S107.3.B. The following statement can be made

regarding the factor area: There is enough evidence to reject the null hypothesis

and accept the alternative hypothesis that a significant difference exists for SLA

S107.2.I3. The following statement can be made regarding the factor region:

There is enough evidence to reject the null hypothesis and accept the alternative

106

hypothesis that a significant difference exists for SLAs S103.1.3, S103.3.1,

S103.3.2, S103.4, S103.6.B3, and S105.A. The following statement can be

made regarding the factor group: There is enough evidence to reject the null


exists for SLAs S101.B1, S103.1.3, S103.3.1, S103.3.2, S103.4, S103.6.B1,

S103.6.B3, S107.2.PL1, and S107.3.B.

Each factor is significant for at least one SLA. Half of the time not all

significant factors contain significant levels. Level L in factor site size is superior

in performance for SLAs S103.3.2 and S107.3.B. In factor region, levels

NORTHEAST and NORTHWEST are both superior in performance for SLAs

S103.1.3 and S103.3.1. Sixteen levels are performing inferiorly when compared

to others in 23 SLAs.

Many factors contribute significantly to data variably. For example, 11

factors account for or contribute to at least 25% of the of data variability within

their respective SLAs. Factor Group accounts for 38.22% of data variability in

SLA S103.1.3. Factor Region accounts for 42.87% of data variability in SLA

S103.3.1. Factor Group accounts for 59.10% of data variability in SLA S103.3.1.

Factor Site Size accounts for 40.67% of data variability in SLA S103.3.2. Factor

Group accounts for 34.14% of data variability in SLA S103.3.2. Factor Server

Farm accounts for 36.53% of data variability in SLA S103.3.2. Factor Region

accounts for 29.98% of data variability in SLA S103.4. Factor Group accounts

for 29.55% of data variability in SLA S103.4. Factor Group accounts for 54.43%

of data variability in SLA S103.6 B3. Factor Site Size accounts for 34.16% of

107

data variability in SLA S107.3.B. Factor Group accounts for 50.71% of data

variability in SLA S107.3.B.

In general, the normality checks displayed indications that nonnormal

behavior is evident for most of the data sets. Notwithstanding the fact that the

data were transformed in most cases, eleven factors, which were identified as

significant, displayed poorly shaped normal residual curves. Twelve factors

displayed many outliers in their data sets, and only four factors contained equal

variance. These results indicate or suggest that the methods used to capture

those data and/or the factor processes are not behaving normally. Investigation

into the cause of this nonnormal behavior is warranted.

The original statement of problem suggests that some factors may be

affecting the SLA metrics in the NMCI network. The results of the analysis

support the statement that within the NMCI network, disparate factors are

affecting the service level metrics as defined in the service level agreements.

The significant levels in the significant factors are either inferior or superior in

performance when compared to others. In each case, there is room for

improvement. The inferior levels are performing poorly for a reason. Similarly, the

superior levels are performing better for yet another reason. In either case,

investigation that includes future analysis and tracking is warranted.

108

Recommendations

Based on the analyses, findings, and conclusions drawn from this study,

this researcher recommends the following actions be conducted in an effort to

not only improve NMCI service quality, but also preclude future trends that can

degrade service quality:

1. Continue to analyze future NMCI SLA data sets based on the

methodology outlined in this report. Action will detect significant factor trends

over time.

2. Investigate and attempt to determine why the superior levels are

outperforming others in their respective group. Action may provide feedback to

help increase inferior level performance.

3. Investigate and attempt to determine why the inferior levels are not

performing as well as others in their respective group. Action may provide

feedback to help increase inferior level performance.

4. Should attempt to investigate and understand the root cause for the

significant factors and levels identified in this study. Action should provide insight

as to why the factors and levels are significant.

5. Review data collection methods and practices. Action may provide

answers that can explain and possibly preclude the nonnormal behavior in the

data sets.

109

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113

APPENDICES

114

Appendix A

Analysis Summary Sheet

115

Appendix A

Variables Trans. No. Sites Analysis Results

SLA Factor Type Testable

Data Points Method Factor

Significant? F Value P Value R²

(adj%)

S101.B1 Site Size Arcsin 318 MANOVA No 1.35 0.234 N/A

S101.B1 NOC Arcsin 318 MANOVA No 0.51 0.802 N/A

S101.B1 Area Arcsin 318 MANOVA No 0.93 0.428 N/A

S101.B1 Region Arcsin 318 ANOVA No 1.48 0.163 1.20

S101.B1 Group Arcsin 318 ANOVA Yes 1.42 0.031 7.79

S101.B1 Server Farm Arcsin 318 ANOVA No 1.28 0.175 2.08





S101.B2 Group Arcsin 318 ANOVA No 0.54 0.998 0.00

S101.B2 Server Farm Arcsin 318 ANOVA No 0.76 0.789 0.00





S101.B3 Group Arcsin 318 ANOVA No 0.55 0.998 0.00

S101.B3 Server Farm Arcsin 318 ANOVA Yes 1.70 0.023 5.05

S103.1.3 Site Size Arcsin 364 MANOVA No 0.76 0.469 N/A

S103.1.3 NOC Arcsin 364 MANOVA Yes 6.78 0.000 4.28

S103.1.3 Area Arcsin 364 MANOVA No 1.67 0.197 N/A

S103.1.3 Region Arcsin 364 ANOVA Yes 7.51 0.000 12.54

S103.1.3 Group Arcsin 364 ANOVA Yes 4.46 0.000 38.22

S103.1.3 Server Farm Arcsin 364 ANOVA No N/A N/A N/A

S103.3.1 Site Size Arcsin 364 MANOVA No 0.25 0.776 N/A

S103.3.1 NOC Arcsin 364 MANOVA Yes 2.67 0.047 1.94

S103.3.1 Area Arcsin 364 MANOVA No 1.75 0.187 N/A



S103.3.1 Group Arcsin 364 ANOVA Yes 9.07 0.000 59.10

S103.3.1 Server Farm Arcsin 364 ANOVA No N/A N/A N/A

S103.3.2 Site Size None 256 MANOVA Yes 178.56 0.000 40.67

S103.3.2 NOC None 256 MANOVA No 2.20 0.113 N/A

S103.3.2 Area None 256 MANOVA No 3.13 0.078 N/A

S103.3.2 Region None 256 ANOVA Yes 7.71 0.000 17.38

S103.3.2 Group None 256 ANOVA Yes 3.13 0.000 34.14

S103.3.2 Server Farm None 256 ANOVA Yes 7.11 0.000 36.53

116

Appendix A


SLA Factor Type

Testable Data

Points Method Factor

Significant? F Value P Value R² (adj%)

S103.4 Site Size Arcsin 364 MANOVA No 0.57 0.566 N/A

S103.4 NOC Arcsin 364 MANOVA Yes 11.31 0.000 8.34

S103.4 Area Arcsin 364 MANOVA No 0.05 0.816 N/A

S103.4 Region Arcsin 364 ANOVA Yes 20.43 0.000 29.98

S103.4 Group Arcsin 364 ANOVA Yes 3.34 0.000 29.55

S103.4 Server Farm Arcsin 364 ANOVA No N/A N/A N/A

S103.6 B1 Site Size Arcsin 153 MANOVA No 0.35 0.790 N/A

S103.6 B1 NOC Arcsin 153 MANOVA No 0.27 0.953 N/A

S103.6 B1 Area Arcsin 153 MANOVA No 0.44 0.727 N/A

S103.6 B1 Region Arcsin 153 ANOVA No 1.57 0.138 2.92

S103.6 B1 Group Arcsin 153 ANOVA Yes 1.47 0.047 16.13

S103.6 B1 Server Farm Arcsin 153 ANOVA No 1.23 0.230 3.48




S103.6 B2 Region Arcsin 153 ANOVA No 0.82 0.585 0.00

S103.6 B2 Group Arcsin 153 ANOVA No 1.20 0.214 7.50





S103.6 B3 Region Arcsin 153 ANOVA Yes 2.53 0.013 7.46

S103.6 B3 Group Arcsin 153 ANOVA Yes 3.93 0.000 54.43


S105.A Site Size Arcsin 311 MANOVA No 1.91 0.150 N/A

S105.A NOC Arcsin 311 MANOVA Yes 5.64 0.000 6.65

S105.A Area Arcsin 311 MANOVA No 0.72 0.489 N/A

S105.A Region Arcsin 311 ANOVA Yes 2.35 0.018 3.36

S105.A Group Arcsin 311 ANOVA No 0.72 0.937 0.00

S105.A Server Farm Arcsin 311 ANOVA 1.55 0.052 4.05

S105.B Site Size Arcsin 311 MANOVA No 1.91 0.150 N/A

S105.B NOC Arcsin 311 MANOVA Yes 5.64 0.000 3.60

S105.B Area Arcsin 311 MANOVA No 0.72 0.489 N/A

S105.B Region Arcsin 311 ANOVA No 1.16 0.325 0.41

S105.B Group Arcsin 311 ANOVA No 0.35 1.000 0.00

S105.B Server Farm Arcsin 311 ANOVA No 1.35 1.330 2.61

117

Appendix A


SLA Factor Type

Testable Data

Points Method Factor

Significant? F Value P Value R² (adj%)

S107.1.A Site Size Arcsin 303 MANOVA No 2.55 0.112 N/A

S107.1.A NOC Arcsin 303 MANOVA No 0.10 0.903 N/A

S107.1.A Area Arcsin 303 MANOVA No 0.00 0.966 N/A

S107.1.A Region Arcsin 303 ANOVA No 1.13 0.341 0.35

S107.1.A Group Arcsin 303 ANOVA No 0.61 0.990 0.00

S107.1.A Server Farm Arcsin 303 ANOVA No 0.65 0.899 0.00

S107.2.l2 Site Size Arcsin 138 MANOVA No 2.06 0.153 N/A

S107.2.l2 NOC Arcsin 138 MANOVA No 0.17 0.842 N/A

S107.2.l2 Area Arcsin 138 MANOVA No 0.36 0.551 N/A

S107.2.l2 Region Arcsin 138 ANOVA No 0.55 0.818 0.00

S107.2.l2 Group Arcsin 138 ANOVA No 0.95 0.572 0.00

S107.2.l2 Server Farm Arcsin 138 ANOVA No 0.76 0.773 0.00

S107.2.l3 Site Size Arcsin 166 MANOVA No 1.31 0.254 N/A

S107.2.l3 NOC Arcsin 166 MANOVA Yes 22.71 0.000 15.84

S107.2.l3 Area Arcsin 166 MANOVA Yes 9.71 0.002 0.00

S107.2.l3 Region Arcsin 166 ANOVA No 0.24 0.974 0.00

S107.2.l3 Group Arcsin 166 ANOVA No 0.07 1.000 0.00

S107.2.l3 Server Farm Arcsin 166 ANOVA Yes 1.73 0.035 8.10

S107.2.PL1 Site Size Arcsin 138 MANOVA No 0.11 0.745 N/A

S107.2.PL1 NOC Arcsin 138 MANOVA No 0.02 0.976 N/A

S107.2.PL1 Area Arcsin 138 MANOVA No 0.07 0.789 N/A

S107.2.PL1 Region Arcsin 138 ANOVA No 1.67 0.106 1.74

S107.2.PL1 Group Arcsin 138 ANOVA Yes 2.16 0.000 19.93

S107.2.PL1 Server Farm Arcsin 138 ANOVA No 1.30 0.162 2.33

S107.3.A Site Size Arcsin 112 MANOVA No 3.88 0.051 N/A

S107.3.A NOC Arcsin 112 MANOVA No 1.87 0.159 N/A

S107.3.A Area Arcsin 112 MANOVA No 3.35 0.070 N/A

S107.3.A Region Arcsin 112 ANOVA No 1.06 0.390 0.34

S107.3.A Group Arcsin 112 ANOVA No 0.59 0.908 0.00

S107.3.A Server Farm Arcsin 112 ANOVA No 0.77 0.718 0.00

S107.3.B Site Size Arcsin 112 MANOVA Yes 60.70 0.000 34.16

S107.3.B Site Size Arcsin 112 MANOVA Yes 60.70 0.000 34.16

S107.3.B NOC Arcsin 112 MANOVA Yes 3.10 0.049 2.19

S107.3.B Area Arcsin 112 MANOVA No 2.84 0.095 N/A

S107.3.B Region Arcsin 112 ANOVA No 2.17 0.052 5.95

S107.3.B Group Arcsin 112 ANOVA Yes 6.71 0.000 50.71

S107.3.B Server Farm Arcsin 112 ANOVA Yes 3.20 0.000 24.07

118

Appendix B

Post-Hoc Summary Sheet

119

Variables Normality Checks Post-Hoc Test Results

SLA Factor

Residuals / Normal

Curve Extreme Outliers?

Equal Variance?

Significant Level?

Disparate Level(s)

Relationship to others in

level

Individual Confidence

Level %

S101.B1 Site Size N/A N/A N/A N/A N/A N/A

S101.B1 NOC N/A N/A N/A N/A N/A N/A

S101.B1 Area N/A N/A N/A N/A N/A N/A

S101.B1 Region N/A N/A N/A N/A N/A N/A

S101.B1 Group Good Few No Yes MILLINGTON Inferior 99.99

S101.B1 Server Farm N/A N/A N/A N/A N/A N/A





S101.B2 Group N/A N/A N/A N/A N/A N/A

S101.B2 Server Farm N/A N/A N/A N/A N/A N/A





S101.B3 Group N/A N/A N/A N/A N/A N/A

S101.B3 Server Farm Marginal Many No Yes LTLC Inferior 99.97

S103.1.3 Site Size N/A N/A N/A N/A N/A N/A

S103.1.3 NOC Poor Few Yes No N/A N/A

S103.1.3 Area N/A N/A N/A N/A N/A N/A

S103.1.3 Region Marginal Few No Yes NORTHEAST, NORTHWEST Superior 99.79

S103.1.3 Group Good Few N/A Yes

POINT MUGU, MCAS BEAUFORT Inferior N/A

S103.1.3 Server Farm N/A N/A N/A N/A N/A N/A

S103.3.1 Site Size N/A N/A N/A N/A N/A N/A

S103.3.1 NOC Poor One No No N/A N/A


S103.3.1 Region Marginal Few No Yes HAWAII Inferior 99.79

S103.3.1 Region Marginal Few No Yes NORTHEAST, NORTHWEST Superior 99.79

S103.3.1 Group Marginal Few Yes Yes

CHARLESTON, MAKALAPA, BEAUFORT, PARRIS ISLAND, PEARL HARBOR Inferior 100.00

S103.3.1 Server Farm N/A N/A N/A N/A N/A N/A

S103.3.2 Site Size Good Many No Yes L Superior 95.00

S103.3.2 NOC N/A N/A N/A N/A N/A N/A


S103.3.2 Region Good Many No No N/A N/A

S103.3.2 Group Good Few No No N/A N/A

S103.3.2 Server Farm Good Many No Yes NWOR Inferior 99.97

Appendix B

120


SLA Factor

Residuals / Normal


Equal Variance?

Significant Level?

Disparate Level(s)


level


Level %

S103.4 Site Size N/A N/A N/A N/A N/A N/A

S103.4 NOC Poor Yes No No N/A N/A

S103.4 Area N/A N/A N/A N/A N/A N/A

S103.4 Region Poor Yes No No N/A N/A

S103.4 Group Poor No No Yes MAKALAPA Inferior 100.00

S103.4 Server Farm N/A N/A N/A N/A N/A N/A

S103.6 B1 Site Size N/A N/A N/A N/A N/A N/A

S103.6 B1 NOC N/A N/A N/A N/A N/A N/A

S103.6 B1 Area N/A N/A N/A N/A N/A N/A

S103.6 B1 Region N/A N/A N/A N/A N/A N/A

S103.6 B1 Group Marginal Few Yes No N/A N/A

S103.6 B1 Server Farm N/A N/A N/A N/A N/A N/A




S103.6 B2 Region N/A N/A N/A N/A N/A N/A

S103.6 B2 Group N/A N/A N/A N/A N/A N/A





S103.6 B3 Region Poor Few No Yes USMC - EAST Inferior 99.80

S103.6 B3 Group Poor Few No Yes

MCAS CHERRY POINT, MILLINGTON Inferior 99.99


S105.A Site Size N/A N/A N/A N/A N/A N/A

S105.A NOC Poor Many No Yes PRLH Inferior 98.01

S105.A Area N/A N/A N/A N/A N/A N/A

S105.A Region Marginal Many No Yes HAWAII Inferior 99.79

S105.A Group N/A N/A N/A N/A N/A N/A

S105.A Server Farm N/A N/A N/A N/A N/A N/A

S105.B Site Size N/A N/A N/A N/A N/A N/A

S105.B NOC Poor Many No Yes PRLH Inferior 98.01

S105.B Area N/A N/A N/A N/A N/A N/A

S105.B Region N/A N/A N/A N/A N/A N/A

S105.B Group N/A N/A N/A N/A N/A N/A

S105.B Server Farm N/A N/A N/A N/A N/A N/A

Appendix B

121


SLA Factor

Residuals / Normal


Equal Variance?

Significant Level?

Disparate Level(s)


level


Level %

S107.1.A Site Size N/A N/A N/A N/A N/A N/A

S107.1.A NOC N/A N/A N/A N/A N/A N/A S107.1.A Area N/A N/A N/A N/A N/A N/A S107.1.A Region N/A N/A N/A N/A N/A N/A S107.1.A Group N/A N/A N/A N/A N/A N/A

S107.1.A Server Farm N/A N/A N/A N/A N/A N/A

S107.2.L2 Site Size N/A N/A N/A N/A N/A N/A

S107.2.L2 NOC N/A N/A N/A N/A N/A N/A S107.2.L2 Area N/A N/A N/A N/A N/A N/A S107.2.L2 Region N/A N/A N/A N/A N/A N/A S107.2.L2 Group N/A N/A N/A N/A N/A N/A

S107.2.L2 Server Farm N/A N/A N/A N/A N/A N/A

S107.2.L3 Site Size N/A N/A N/A N/A N/A N/A

S107.2.L3 NOC Poor Many No Yes PRLH Inferior 98.10 S107.2.L3 Area Poor Many No No N/A N/A

S107.2.L3 Region N/A N/A N/A N/A N/A N/A S107.2.L3 Group N/A N/A N/A N/A N/A N/A

S107.2.L3 Server Farm Poor Few No Yes BREM Inferior 99.96

S107.2.PL1 Site Size N/A N/A N/A N/A N/A N/A

S107.2.PL1 NOC N/A N/A N/A N/A N/A N/A S107.2.PL1 Area N/A N/A N/A N/A N/A N/A

S107.2.PL1 Region N/A N/A N/A N/A N/A N/A

S107.2.PL1 Group Good Many No Yes

ANACOSTIA, MCAS CHERRY POINT Inferior 100.00

S107.2.PL1 Server Farm N/A N/A N/A N/A N/A N/A

S107.3.A Site Size N/A N/A N/A N/A N/A N/A

S107.3.A NOC N/A N/A N/A N/A N/A N/A S107.3.A Area N/A N/A N/A N/A N/A N/A S107.3.A Region N/A N/A N/A N/A N/A N/A

S107.3.A Group N/A N/A N/A N/A N/A N/A

S107.3.A Server Farm N/A N/A N/A N/A N/A N/A

S107.3.B Site Size Good Few No Yes S Inferior 95.00

S107.3.B Site Size Good Few No Yes L Superior 95.00

S107.3.B NOC Good Few Yes No N/A N/A

S107.3.B Area N/A N/A N/A N/A N/A N/A S107.3.B Region N/A N/A N/A N/A N/A N/A

S107.3.B Group Good Few No No N/A 99.96

S107.3.B Server Farm Good One No No N/A 99.94

Appendix B

122

Appendix C

Sample Box Plots

123

Server Farm

T101a

WNY

D

SPSC

SMTH

SDNS

SDNI

PRTH

PRLH

PHIL

PAXR

ORL

O

OCE

N

NWOR

NRFK

MUG

UMILL

MEC

HLT

LCLKHR

LEMR

JAXS

FALN

CRAN

CHRL

CHLK

BREM

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Boxplot of T101a by Server Farm

124

Appendix D

Sample Residual / Normal Probability Plots

125

Residual

Percent

0.5

0.0

-0.5

-1.0

-1.599.99990501010.1

Fitted Value

Residual

1.41.31.21.1

0.5

0.0

-0.5

-1.0

-1.5

Residual

Frequency

0.30.0-0.3-0.6-0.9-1.2

80

60

40

20

0

Observation Order

Residual

280

260

240

220

200

180

160

140

120

100806040201

0.5

0.0

-0.5

-1.0

-1.5

Normal Probability Plot of the Residuals Residuals Versus the Fitted Values

Histogram of the Residuals Residuals Versus the Order of the Data

Residual Plots for T101a

Residual

Percent

0.5

0.0

-0.5

-1.0

-1.5

99.99995908070605040302010510.1

Normal Probability Plot of the Residuals(response is T101a)

126

Appendix E

Sample Nonnormal Probability Plot

127

Residual

Percent

0.5

0.0

-0.5

-1.099.99990501010.1

Fitted Value

Residual

0.930.920.910.900.89

0.0

-0.2

-0.4

-0.6

-0.8

Residual

Frequency

0.00-0.15-0.30-0.45-0.60-0.75-0.90

80

60

40

20

0

Observation Order

Residual

280

260

240

220

200

180

160

140

120

100806040201

0.0

-0.2

-0.4

-0.6

-0.8



Residual Plots for S101a

Residual

Percent

0.50

0.25

0.00

-0.25

-0.50

-0.75

-1.00

99.99995908070605040302010510.1

Normal Probability Plot of the Residuals(response is S101a)

128

Appendix F

Coded Levels

129

Appendix F

Site Size (Literal level) Translation

L Large Site

S Small Site

VSSD Very Small Site Design

Server Farm (Literal level) Translation

BREM NS Bremerton, WA

CHLK NAWS China Lake, CA

CHRL NWS Charleston, SC

CRAN NSWC Crane, IN

FALN NAS Fallon, NV

JAXS NAS Jacksonville, FL

LEMR NAS Lemoore, CA

LKHR NAES Lakehurst, NJ

LTLC NAB Little Creek, VA

MECH NSA Mechanicsburg, PA

MILL NSA Mid South, Millington, TN

MUGU NBVC Point Mugu, CA

NRFK NS Norfolk, VA

NWOR NSA New Orleans East Bank, LA

130

Appendix F

Server Farm (Literal level) Translation

OCEN NAS Oceana, Virginia Beach, VA

ORLO NAWCTSD Orlando, FL

PAXR NAS Patuxent River, MD

PHIL NSA Philadelphia, PA

PRLH NC Pearl Harbor, HI

PRTH NBVC Port Hueneme, CA

SDNI NAS North Island, CA

SDNS NS San Diego, CA

SMTH MCB Camp HM Smith, HI

SPSC NC Point Loma, San Diego, CA

WNYD Washington Navy Yard, DC

Network Operations Center (Literal level) Translation

NRFK NS Norfolk, VA

PRLH NC Pearl Harbor, HI

QUAN MCB Quantico, VA

SDNI NAS North Island, CA

131

Appendix G

Tukey’s Test Results

132

Figure 3. Tukey’s test for SLA S101.B1 for factor group.

Appendix G

133

Figure 4. Tukey’s test for SLA S101.B3 for factor server farm.

Appendix G

134

Figure 5. Tukey’s test for SLA S103.1.3 for factor region.

Appendix G

135

Figure 6. Tukey’s test for SLA S103.1.3 for factor group part A.

Appendix G

136

Figure 7. Tukey’s test for SLA S103.1.3 for factor group part B.

Appendix G

137

Figure 8. Tukey’s test for SLA S103.3.1 for factor region part A.

Appendix G

138

Figure 9. Tukey’s test for SLA S103.3.1 for factor region part B.

Appendix G

139

Figure 10. Tukey’s test for SLA S103.3.1 for factor group part C.

Appendix G

140

Figure 11. Tukey’s test for SLA S103.3.1 for factor group part D.

Appendix G

141

Figure 12. Tukey’s test for SLA S103.3.1 for factor group part E.

Appendix G

142

Figure 13. Tukey’s test for SLA S103.3.1 for factor group part F.

Appendix G

143

Figure 14. Tukey’s test for SLA S103.3.1 for factor group part G.

Appendix G

144

Figure 15. Tukey’s test for SLA S103.3.2 for factor site size.

Figure 16. Tukey’s test for SLA S103.3.2 for factor server farm.

Appendix G

145

Figure 17. Tukey’s test for SLA S103.4 for factor group.

Appendix G

146

Figure 18. Tukey’s test for SLA S103.6.B3 for factor region.

Appendix G

147

Figure 19. Tukey’s test for SLA S103.6.B3 for factor group part A.

Appendix G

148

Figure 20. Tukey’s test for SLA S103.6.B3 for factor group part B.

Appendix G

149

Tukey 95% Simultaneous Confidence Intervals

All Pairwise Comparisons among Levels of NOC

Individual confidence level = 98.01%

NOC = NRFK subtracted from:

NOC Lower Center Upper --------+---------+---------+---------+-

PRLH -0.3431 -0.2299 -0.1168 (-----*----)

SDNI -0.0768 0.0036 0.0839 (---*---)

--------+---------+---------+---------+-

-0.20 0.00 0.20 0.40

NOC = PRLH subtracted from:

NOC Lower Center Upper --------+---------+---------+---------+-

SDNI 0.1105 0.2335 0.3565 (-----*-----)

--------+---------+---------+---------+-

-0.20 0.00 0.20 0.40

Figure 21. Tukey’s test for SLA S105.A for factor NOC.

Figure 22. Tukey’s test for SLA S105.A for factor region.

Appendix G

150

Figure 23. Tukey’s test for SLA S105.B for factor NOC.

Appendix G

151

Appendix H

Variance Test Results

152

Site Group

95% Bonferroni Confidence Intervals for StDevs

YUMAYORKTOWN

WNYWHIDBEY ISLANDVIRGINIA BEACH

REMOTE SEREMOTE S/ SW

REMOTE NC/ NW/ OKLAHOMAREMOTE MW/ NE

REMOTE MS/NEW ORLEANSPORTSMOUTH-NH SPYD

PORT HUENEMEPOINT MUGU

PHILADELPHIAPENTAGONPENSACOLA

PEARL HARBORPATUXENT RIVER

PANAMA CITYORLANDO

OLD TOWNNSY NORFOLKNSA NORFOLK

NS POINT LOMANS NORFOLK

NORTH ISLANDNEWPORT

NEBRASKA AVE.NAVY ANNEX

NAVAL BASE SAN DIEGONAS JRB FORT WORTH

NAF WASHINGTONMILLINGTON

MECHANICSBURGMCAS MIRAMAR

MCAS CHERRY POINTMCAS BEAUFORT

MAYPORTMAKALAPA

LOS ANGELESLITTLE CREEK

LEMOORELAKEHURSTKINGS BAY

KANEOHE BAYJACKSONVILLEINDIAN HEAD

GULFPORT/MERIDIANGROTON

GREAT LAKESFALLON

DAHLGRENCRYSTAL CITY

CRANECORPUS CHRISTI

CHINA LAKECHARLESTON-SC

CARDEROCKCAMP SMITH

CAMP PENDLETONCAMP LEJEUNE

BRUNSWICKBREMERTON

BANGORANACOSTIA

10008006004002000

Test Statistic 94.69

P-Value 0.000

Test Statistic 1.66

P-Value 0.014

Bartlett's Test

Levene's Test

Test for Equal Variances for T101.B1

Figure 24. Equal variance test for SLA S101.B1 for factor group.

Appendix H

153

Server Farm


WNYDSPSCSMTHSDNSSDNIPRTHPRLHPHILPAXRORLOOCENNWORNRFK

MUGUMILL

MECHLTLCLKHRLEMRJAXSFALNCRANCHRLCHLKBREM

2.52.01.51.00.50.0


P-Value 0.000

Test Statistic 1.90

P-Value 0.011

Bartlett's Test

Levene's Test

Test for Equal Variances for T101.B3

Figure 25. Equal variance test for SLA S101.B3 for factor server farm.

Appendix H

154

NOC


SDNI

QUAN

PRLH

NRFK

0.0140.0130.0120.0110.0100.009

NOC

T103.1.3

SDNI

QUAN

PRLH

NRFK

1.571.561.551.541.53

Test Statistic 0.74

P-Value 0.066

Test Statistic 9.51

P-Value 0.000

F-Test

Levene's Test

Test for Equal Variances for T103.1.3

Figure 26. Equal variance test for SLA S103.1.3 for factor NOC.

Appendix H

155

Region


USMC - WEST

USMC - EAST

TIDEWATER

SOUTHWEST

SOUTHEAST

NORTHWEST

NORTHEAST

NCR

HAWAII

0.040.030.020.010.00


P-Value 0.000

Test Statistic 7.51

P-Value 0.000

Bartlett's Test

Levene's Test


Figure 27. Equal variance test for SLA S103.1.3 for factor region.

Appendix H

156

Site Group


YUMAYORKTOWN








PARRIS ISLANDPANAMA CITY

ORLANDOOLD TOWN

NSY NORFOLKNSA NORFOLK


NORTH ISLANDNEWPORT






MAYPORTMAKALAPA





GREAT LAKESFALLON


CRANECORPUS CHRISTI


CARDEROCKCAMP SMITH


BRUNSWICKBREMERTON

BANGORANACOSTIA

543210


P-Value 0.000

Test Statistic 2.82

P-Value 0.000

Bartlett's Test

Levene's Test


Figure 28. Equal variance test for SLA S103.1.3 for factor group.

Appendix H

157

NOC


SDNI

QUAN

PRLH

NRFK

0.0400.0350.0300.0250.0200.0150.010


P-Value 0.000

Test Statistic 3.13

P-Value 0.045

Bartlett's Test

Levene's Test


Figure 29. Equal variance test for SLA S103.3.1 for factor NOC.

Appendix H Appendix H

158

Region


USMC - WEST

USMC - EAST

TIDEWATER

SOUTHWEST

SOUTHEAST

NORTHWEST

NORTHEAST

NCR

HAWAII

0.070.060.050.040.030.020.010.00


P-Value 0.000


P-Value 0.000

Bartlett's Test

Levene's Test



Appendix H

159

Site Group


YUMAYORKTOWN









ORLANDOOLD TOWN



NORTH ISLANDNEWPORT






MAYPORTMAKALAPA





GREAT LAKESFALLON


CRANECORPUS CHRISTI


CARDEROCKCAMP SMITH


BRUNSWICKBREMERTON

BANGORANACOSTIA

181614121086420


P-Value 0.000

Test Statistic 8.40

P-Value 0.000

Bartlett's Test

Levene's Test



Appendix H

160

Site Size


S

L

2.752.502.252.001.751.50

Site Size

S103.3.2

S

L

14121086420

Test Statistic 0.51

P-Value 0.000


P-Value 0.000

F-Test

Levene's Test

Test for Equal Variances for S103.3.2

Figure 32. Equal variance test for SLA S103.3.2 for factor site size.

Appendix H

161

Region


USMC - WEST

USMC - EAST

TIDEWATER

SOUTHWEST

SOUTHEAST

NORTHWEST

NORTHEAST

NCR

HAWAII

2520151050


P-Value 0.000

Test Statistic 3.51

P-Value 0.001

Bartlett's Test

Levene's Test



Appendix H

162

Site Group


YUMAYORKTOWN




PORTSMOUTH-NH SPYDPORT HUENEME

POINT MUGUPHILADELPHIA

PENTAGONPENSACOLA


PANAMA CITYORLANDO



NORTH ISLANDNEWPORT






MAYPORTMAKALAPA





GREAT LAKESFALLON


CRANECORPUS CHRISTI


CARDEROCKCAMP SMITH

CAMP PENDLETONBRUNSWICKBREMERTON

BANGORANACOSTIA

500040003000200010000


P-Value 0.000

Test Statistic 1.41

P-Value 0.087

Bartlett's Test

Levene's Test



Appendix H

163

Server Farm


WNYDSPSCSMTHSDNSSDNIPRTHPRLHPHILPAXRORLOOCENNWORNRFK

MUGUMILL

MECHLTLCLKHRLEMRJAXSFALNCRANCHRLCHLKBREM

5004003002001000


P-Value 0.000

Test Statistic 1.38

P-Value 0.130

Bartlett's Test

Levene's Test


Figure 35. Equal variance test for SLA S103.3.2 for factor server farm.

Appendix H

164

NOC


SDNI

QUAN

PRLH

NRFK

0.0090.0080.0070.0060.0050.004

NOC

T103.4

SDNI

QUAN

PRLH

NRFK

1.571.561.551.541.53

Test Statistic 0.56

P-Value 0.009


P-Value 0.000

F-Test

Levene's Test

Test for Equal Variances for T103.4

Figure 36. Equal variance test for SLA S103.4 for factor NOC.

Appendix H

165

Region


USMC - WEST

USMC - EAST

TIDEWATER

SOUTHWEST

SOUTHEAST

NORTHWEST

NORTHEAST

NCR

HAWAII

0.0200.0150.0100.005


P-Value 0.000

Test Statistic 2.61

P-Value 0.009

Bartlett's Test

Levene's Test


Figure 37. Equal variance test for SLA S103.4 for factor region.

Appendix H

166

Site Group


YUMAYORKTOWN









ORLANDOOLD TOWN



NORTH ISLANDNEWPORT






MAYPORTMAKALAPA





GREAT LAKESFALLON


CRANECORPUS CHRISTI


CARDEROCKCAMP SMITH


BRUNSWICKBREMERTON

BANGORANACOSTIA

0.090.080.070.060.050.040.030.020.010.00


P-Value 0.003

Test Statistic 1.47

P-Value 0.036

Bartlett's Test

Levene's Test


Figure 38. Equal variance test for SLA S103.4 for factor group.

Appendix H

167

Site Group


YORKTOWNWNY

WHIDBEY ISLANDVIRGINIA BEACH







PANAMA CITYORLANDO



NORTH ISLANDNEWPORT





MCAS CHERRY POINTMAYPORT

MAKALAPALOS ANGELES

LITTLE CREEKLEMOORE

LAKEHURSTKINGS BAY



GREAT LAKESFALLON


CRANECORPUS CHRISTI


CARDEROCKCAMP SMITH


BRUNSWICKBREMERTON

BANGORANACOSTIA

5004003002001000


P-Value 0.235

Test Statistic 1.21

P-Value 0.248

Bartlett's Test

Levene's Test

Test for Equal Variances for T103.6.B1

Figure 39. Equal variance test for SLA S103.6.B1 for factor group.

Appendix H

168

Region


USMC - WEST

USMC - EAST

TIDEWATER

SOUTHWEST

SOUTHEAST

NORTHWEST

NORTHEAST

NCR

HAWAII

1.00.80.60.40.20.0


P-Value 0.000

Test Statistic 2.71

P-Value 0.008

Bartlett's Test

Levene's Test

Test for Equal Variances for S103.6.B3

Figure 40. Equal variance test for SLA S103.6.B3 for factor region.

Appendix H

169

Site Group


YORKTOWNWNY

WHIDBEY ISLANDVIRGINIA BEACH







PANAMA CITYORLANDO



NORTH ISLANDNEWPORT





MCAS CHERRY POINTMAYPORT

MAKALAPALOS ANGELES

LITTLE CREEKLEMOORE

LAKEHURSTKINGS BAY



GREAT LAKESFALLON


CRANECORPUS CHRISTI


CARDEROCKCAMP SMITH


BRUNSWICKBREMERTON

BANGORANACOSTIA

6050403020100

Test Statistic 5.36

P-Value 0.147


P-Value 0.000

Bartlett's Test

Levene's Test

Test for Equal Variances for T103.6.B3

Figure 41. Equal variance test for SLA S103.6.B3 for factor group.

Appendix H

170

NOC


SDNI

PRLH

NRFK

0.80.70.60.50.40.30.20.1


P-Value 0.000


P-Value 0.000

Bartlett's Test

Levene's Test

Test for Equal Variances for T105.A

Figure 42. Equal variance test for SLA S105.A for factor NOC.

Appendix H

171

Region


USMC - WEST

USMC - EAST

TIDEWATER

SOUTHWEST

SOUTHEAST

NORTHWEST

NORTHEAST

NCR

HAWAII

1.21.00.80.60.40.20.0


P-Value 0.000

Test Statistic 2.21

P-Value 0.027

Bartlett's Test

Levene's Test

Test for Equal Variances for T105.A

Figure 43. Equal variance test for SLA S105.A for factor region.

Appendix H

172

NOC


SDNI

PRLH

NRFK

0.60.50.40.30.20.1


P-Value 0.000

Test Statistic 6.79

P-Value 0.001

Bartlett's Test

Levene's Test

Test for Equal Variances for T105.B

Figure 44. Equal variance test for SLA S105.B for factor NOC.

Appendix H

173

NOC


SDNI

PRLH

NRFK

1.21.00.80.60.40.20.0


P-Value 0.000


P-Value 0.000

Bartlett's Test

Levene's Test

Test for Equal Variances for T107.2.L3

Figure 45. Equal variance test for SLA S107.2.l3 for factor NOC.

Appendix H

174

Area


West

East

0.2750.2500.2250.2000.1750.150

Area

T107.2.L3

West

East

1.751.501.251.000.750.500.250.00

Test Statistic 0.58

P-Value 0.015

Test Statistic 0.71

P-Value 0.399

F-Test

Levene's Test


Figure 46. Equal variance test for SLA S107.2.L3 for factor area.

Appendix H

175

Server Farm


WNYD

SPSC

SDNS

SDNI

PRLH

PHIL

ORLO

OCEN

NWOR

NRFK

MUGU

MILL

MECH

LTLC

LEMR

JAXS

FALN

CRAN

CHRL

CHLK

BREM

876543210


P-Value 0.000

Test Statistic 2.29

P-Value 0.006

Bartlett's Test

Levene's Test


Figure 47. Equal variance test for SLA S107.2.L3 for factor server farm.

Appendix H

176

Site Group


YUMAYORKTOWN









ORLANDOOLD TOWN



NORTH ISLANDNEWPORT






MAYPORTMAKALAPA





GREAT LAKESFALLON


CRANECORPUS CHRISTI


CARDEROCKCAMP SMITH


BRUNSWICKBREMERTON

BANGORANACOSTIA

140120100806040200


P-Value 0.000

Test Statistic 1.21

P-Value 0.203

Bartlett's Test

Levene's Test

Test for Equal Variances for T107.2.PL1

Figure 48. Equal variance test for SLA S107.2.PL1 for factor group.

Appendix H

177

Site Size


S

L

0.500.450.400.350.300.250.20

Site Size

T107.3.B

S

L

1.751.501.251.000.750.500.250.00

Test Statistic 2.37

P-Value 0.005


P-Value 0.000

F-Test

Levene's Test

Test for Equal Variances for T107.3.B

Figure 49. Equal variance test for SLA S107.3.B for factor site size.

Appendix H

178

NOC


SDNI

PRLH

NRFK

1.00.90.80.70.60.50.40.30.20.1

Test Statistic 5.40

P-Value 0.067

Test Statistic 4.68

P-Value 0.011

Bartlett's Test

Levene's Test


Figure 50. Equal variance test for SLA S107.3.B for factor NOC.

Appendix H

179

Site Group


WNY

WHIDBEY ISLAND

REMOTE SE

REMOTE S/ SW

REMOTE NC/ NW/ OKLAHOMA

REMOTE MW/ NE

REMOTE MS/NEW ORLEANS

POINT MUGU

PHILADELPHIA

PENSACOLA

ORLANDO

NEWPORT

MCAS MIRAMAR

LOS ANGELES

LITTLE CREEK

LAKEHURST

GREAT LAKES

CRANE

CORPUS CHRISTI

BREMERTON

BANGOR

76543210


P-Value 0.018

Test Statistic 1.31

P-Value 0.252

Bartlett's Test

Levene's Test


Figure 51. Equal variance test for SLA S107.3.B for factor group.

Appendix H

180

Server Farm


WNYD

SPSC

SDNI

PRTH

PHIL

NWOR

MUGU

MILL

MECH

LTLC

LKHR

JAXS

FALN

CRAN

CHRL

CHLK

BREM

160140120100806040200


P-Value 0.000

Test Statistic 4.03

P-Value 0.000

Bartlett's Test

Levene's Test


Figure 52. Equal variance test for SLA S107.3.B for factor server farm.

Appendix H

181

Appendix I

Residual Plot Results

182

Residual

Percent

0.5

0.0

-0.5

-1.0

-1.5

99.99990501010.1

Fitted Value

Residual

1.2951.2901.2851.2801.275

0.5

0.0

-0.5

-1.0

-1.5

Residual

Frequency

0.30.0-0.3-0.6-0.9-1.2

100

75

50

25

0

Observation Order

Residual

300

280

260

240

220

200

180

160

140

120

100806040201

0.5

0.0

-0.5

-1.0

-1.5



Residual Plots for T101.B1

Figure 53. Residual plots for SLA S101.B1 for factor group.

Appendix I

183

Residual

Percent

0.2

0.0

-0.2

-0.4

-0.6

99.99990501010.1

Fitted Value

Residual

1.561.521.481.441.40

0.2

0.0

-0.2

-0.4

-0.6

Residual

Frequency

0.10.0-0.1-0.2-0.3-0.4-0.5-0.6

200

150

100

50

0

Observation Order

Residual

300

280

260

240

220

200

180

160

140

120

100806040201

0.2

0.0

-0.2

-0.4

-0.6




Figure 54. Residual plots for SLA S101.B3 for factor server farm.

Appendix I

184

Residual

Percent

0.04

0.02

0.00

-0.02

-0.0499.99990501010.1

Fitted Value

Residual

1.5701.5681.5661.5641.562

0.01

0.00

-0.01

-0.02

-0.03

Residual

Frequency

0.0060.000-0.006-0.012-0.018-0.024-0.030

200

150

100

50

0

Observation Order

Residual

350300250200150100501

0.01

0.00

-0.01

-0.02

-0.03



Residual Plots for T103.1.3

Figure 55. Residual plots for SLA S103.1.3 for factor NOC.

Appendix I

185

Residual

Percent

0.04

0.02

0.00

-0.02

-0.0499.99990501010.1

Fitted Value

Residual

1.57001.56751.56501.56251.5600

0.000

-0.015

-0.030

-0.045

Frequency

0.0075

0.0000

-0.0075

-0.0150

-0.0225

-0.0300

-0.0375

160

120

80

40

0

Observation Order

Residual

350300250200150100501

0.000

-0.015

-0.030

-0.045




Figure 56. Residual plots for SLA S103.1.3 for factor region.

Appendix I

186

Residual

Percent

0.04

0.02

0.00

-0.02

-0.0499.99990501010.1

Fitted Value

Residual

1.571.561.551.54

0.04

0.02

0.00

-0.02

-0.04

Residual

Frequency

0.030.020.010.00-0.01-0.02-0.03-0.04

240

180

120

60

0

Observation Order

Residual

350300250200150100501

0.04

0.02

0.00

-0.02

-0.04




Figure 57. Residual plots for SLA S103.1.3 for factor group.

Appendix I

187

Residual

Percent

0.5

0.0

-0.5

-1.0

99.99990501010.1

Fitted Value

Residual

1.501.251.000.750.50

0.5

0.0

-0.5

-1.0

Residual

Frequency

0.60.30.0-0.3-0.6-0.9

100

75

50

25

0

Observation Order

Residual

300

280

260

240

220

200

180

160

140

120

100806040201

0.5

0.0

-0.5

-1.0




Figure 58. Residual plots for SLA S101.B1 for factor group.

Appendix I

188

Residual

Percent

0.050

0.025

0.000

-0.025

-0.050

99.999990501010.01

Fitted Value

Residual

1.561.541.52

0.04

0.02

0.00

-0.02

-0.04

Residual

Frequency

0.030.020.010.00-0.01-0.02-0.03-0.04

300

200

100

0

Observation Order

Residual

700

650

600

550

500

450

400

350

300

250

200

150

100501

0.04

0.02

0.00

-0.02

-0.04





Appendix I

189

Residual

Percent

0.04

0.02

0.00

-0.02

-0.0499.99990501010.1

Fitted Value

Residual

1.581.561.541.521.50

0.04

0.02

0.00

-0.02

-0.04

Residual

Frequency

0.0450.0300.0150.000-0.015-0.030

200

150

100

50

0

Observation Order

Residual

350300250200150100501

0.04

0.02

0.00

-0.02

-0.04





Appendix I

190

Residual

Percent

10

5

0

-5

99.99990501010.1

Fitted Value

Residual

54321

10

5

0

-5

Residual

Frequency

86420-2-4

100

75

50

25

0

Observation Order

Residual

240220200180160140120100806040201

10

5

0

-5



Residual Plots for S103.3.2

Figure 61. Residual plots for SLA S103.3.2 for factor site size.

Appendix I

191

Residual

Percent

10

5

0

-5

99.99990501010.1

Fitted Value

Residual

4321

5

0

-5

Residual

Frequency

86420-2-4

40

30

20

10

0

Observation Order

Residual

240220200180160140120100806040201

5

0

-5





Appendix I

192

Residual

Percent

6

3

0

-3

-6

99.99990501010.1

Fitted Value

Residual

6.04.53.01.50.0

5

0

-5

Residual

Frequency

6.04.53.01.50.0-1.5-3.0

60

45

30

15

0

Observation Order

Residual

240220200180160140120100806040201

5

0

-5





Appendix I

193

Residual

Percent

10

5

0

-5

99.99990501010.1

Fitted Value

Residual

86420

10

5

0

-5

Residual

Frequency

9630-3-6

60

45

30

15

0

Observation Order

Residual

240220200180160140120100806040201

10

5

0

-5




Figure 64. Residual plots for SLA S103.3.2 for factor server farm.

Appendix I

194

Residual

Percent

0.00

-0.02

-0.04

99.99990501010.1

Fitted Value

Residual

1.57081.56961.56841.56721.5660

0.00

-0.01

-0.02

-0.03

-0.04

Residual

Frequency

0.000

-0.006

-0.012

-0.018

-0.024

-0.030

-0.036

-0.042

300

200

100

0

Observation Order

Residual

350300250200150100501

0.00

-0.01

-0.02

-0.03

-0.04



Residual Plots for T103.4

Figure 65. Residual plots for SLA S103.4 for factor NOC.

Appendix I

195

Residual

Percent

0.00

-0.02

-0.04

99.99990501010.1

Fitted Value

Residual

1.57001.56751.56501.56251.5600

0.00

-0.02

-0.04

Residual

Frequency

0.0075

0.0000

-0.0075

-0.0150

-0.0225

-0.0300

-0.0375

300

200

100

0

Observation Order

Residual

350300250200150100501

0.00

-0.02

-0.04




Figure 66. Residual plots for SLA S103.4 for factor region.

Appendix I

196

Residual

Percent

0.000

-0.015

-0.030

-0.045

99.99990501010.1

Fitted Value

Residual

1.5701.5651.5601.555

0.000

-0.015

-0.030

-0.045

Residual

Frequency

0.0080.000-0.008-0.016-0.024-0.032-0.040

300

200

100

0

Observation Order

Residual

350300250200150100501

0.000

-0.015

-0.030

-0.045




Figure 67. Residual plots for SLA S103.4 for factor group.

Appendix I

197

Residual

Percent

0.5

0.0

-0.5

99.99990501010.1

Fitted Value

Residual

1.61.41.21.0

0.5

0.0

-0.5

Residual

Frequency

0.40.20.0-0.2-0.4-0.6

80

60

40

20

0

Observation Order

Residual

150

140

130

120

110

1009080706050403020101

0.5

0.0

-0.5



Residual Plots for T103.6.B1

Figure 68. Residual plots for SLA S103.6.B1 for factor group.

Appendix I

198

Residual

Percent

0.2

0.0

-0.2

-0.4

-0.6

99.99990501010.1

Fitted Value

Residual

1.561.521.481.441.40

0.2

0.0

-0.2

-0.4

-0.6

Residual

Frequency

0.10.0-0.1-0.2-0.3-0.4-0.5-0.6

160

120

80

40

0

Observation Order

Residual

150

140

130

120

110

1009080706050403020101

0.2

0.0

-0.2

-0.4

-0.6




Figure 69. Residual plots for SLA S103.6.B3 for factor region.

Appendix I

199

Residual

Percent

0.4

0.2

0.0

-0.2

-0.499.99990501010.1

Fitted Value

Residual

1.61.41.21.0

0.4

0.2

0.0

-0.2

-0.4

Residual

Frequency

0.30.20.10.0-0.1-0.2-0.3

160

120

80

40

0

Observation Order

Residual

150

140

130

120

110

1009080706050403020101

0.4

0.2

0.0

-0.2

-0.4




Figure 70. Residual plots for SLA S103.6.B3 for factor group.

Appendix I

200

Residual

Percent

0.5

0.0

-0.5

-1.0

-1.5

99.99990501010.1

Fitted Value

Residual

1.451.401.351.301.25

0.5

0.0

-0.5

-1.0

-1.5

Residual

Frequency

0.30.0-0.3-0.6-0.9-1.2-1.5

200

150

100

50

0

Observation Order

Residual

300

280

260

240

220

200

180

160

140

120

100806040201

0.5

0.0

-0.5

-1.0

-1.5



Residual Plots for T105.A

Figure 71. Residual plots for SLA S105.A for factor NOC.

Appendix I

201

Residual

Percent

0.5

0.0

-0.5

-1.0

-1.5

99.99990501010.1

Fitted Value

Residual

1.61.51.41.31.2

0.5

0.0

-0.5

-1.0

-1.5

Residual

Frequency

0.30.0-0.3-0.6-0.9-1.2-1.5

160

120

80

40

0

Observation Order

Residual

300

280

260

240

220

200

180

160

140

120

100806040201

0.5

0.0

-0.5

-1.0

-1.5



Residual Plots for T105.A

Figure 72. Residual plots for SLA S105.A for factor region.

Appendix I

202

Residual

Percent

0.5

0.0

-0.5

-1.0

-1.5

99.99990501010.1

Fitted Value

Residual

1.551.501.451.40

0.0

-0.4

-0.8

-1.2

-1.6

Residual

Frequency

0.0-0.3-0.6-0.9-1.2-1.5

240

180

120

60

0

Observation Order

Residual

300

280

260

240

220

200

180

160

140

120

100806040201

0.0

-0.4

-0.8

-1.2

-1.6



Residual Plots for T105.B

Figure 73. Residual plots for SLA S105.B for factor NOC.

Appendix I

203

Residual

Percent

0.5

0.0

-0.5

-1.0

-1.599.99990501010.1

Fitted Value

Residual

1.61.51.41.3

0.5

0.0

-0.5

-1.0

-1.5

Residual

Frequency

0.30.0-0.3-0.6-0.9-1.2

160

120

80

40

0

Observation Order

Residual

160140120100806040201

0.5

0.0

-0.5

-1.0

-1.5



Residual Plots for T107.2.L3

Figure 74. Residual plots for SLA S107.2.L3 for factor NOC.

Appendix I

204

Residual

Percent

0.5

0.0

-0.5

-1.0

-1.5

99.99990501010.1

Fitted Value

Residual

1.5451.5401.5351.5301.525

0.0

-0.4

-0.8

-1.2

-1.6

Residual

Frequency

0.0-0.2-0.4-0.6-0.8-1.0-1.2-1.4

160

120

80

40

0

Observation Order

Residual

160140120100806040201

0.0

-0.4

-0.8

-1.2

-1.6




Figure 75. Residual plots for SLA S107.2.L3 for factor area.

Appendix I

205

Residual

Percent

0.5

0.0

-0.5

-1.0

-1.599.99990501010.1

Fitted Value

Residual

1.61.51.41.3

0.5

0.0

-0.5

-1.0

Residual

Frequency

0.30.0-0.3-0.6-0.9-1.2

150

100

50

0

Observation Order

Residual

160140120100806040201

0.5

0.0

-0.5

-1.0




Figure 76. Residual plots for SLA S107.2.L3 for factor server farm.

Appendix I

206

Residual

Percent

0.1

0.0

-0.1

-0.2

99.99990501010.1

Fitted Value

Residual

1.601.551.501.451.40

0.1

0.0

-0.1

-0.2

Residual

Frequency

0.120.080.040.00-0.04-0.08-0.12-0.16

100

75

50

25

0

Observation Order

Residual

300

280

260

240

220

200

180

160

140

120

100806040201

0.1

0.0

-0.1

-0.2



Residual Plots for T107.2.PL1

Figure 77. Residual plots for SLA S107.2.PL1 for factor group.

Appendix I

207

Residual

Percent

0.8

0.4

0.0

-0.4

-0.8

99.99990501010.1

Fitted Value

Residual

1.31.21.11.00.9

0.8

0.4

0.0

-0.4

-0.8

Residual

Frequency

0.60.40.20.0-0.2-0.4-0.6-0.8

30

20

10

0

Observation Order

Residual

1101009080706050403020101

0.8

0.4

0.0

-0.4

-0.8



Residual Plots for T107.3.B

Figure 78. Residual plots for SLA S107.3.B for factor site size.

Appendix I

208

Residual

Percent

1.0

0.5

0.0

-0.5

-1.099.99990501010.1

Fitted Value

Residual

1.101.051.000.950.90

0.5

0.0

-0.5

-1.0

Residual

Frequency

0.60.40.20.0-0.2-0.4-0.6-0.8

30

20

10

0

Observation Order

Residual

1101009080706050403020101

0.5

0.0

-0.5

-1.0




Figure 79. Residual plots for SLA S107.3.B for factor NOC.

Appendix I

209

Residual

Percent

0.8

0.4

0.0

-0.4

-0.8

99.99990501010.1

Fitted Value

Residual

1.61.41.21.00.8

0.8

0.4

0.0

-0.4

-0.8

Residual

Frequency

0.60.40.20.0-0.2-0.4-0.6-0.8

40

30

20

10

0

Observation Order

Residual

1101009080706050403020101

0.8

0.4

0.0

-0.4

-0.8




Figure 80. Residual plots for SLA S107.3.B for factor group.

Appendix I

210

Residual

Percent

0.8

0.4

0.0

-0.4

-0.899.99990501010.1

Fitted Value

Residual

1.61.41.21.00.8

0.6

0.3

0.0

-0.3

-0.6

Residual

Frequency

0.60.40.20.0-0.2-0.4-0.6

24

18

12

6

0

Observation Order

Residual

1101009080706050403020101

0.6

0.3

0.0

-0.3

-0.6




Figure 81. Residual plots for SLA S107.3.B for factor server farm.

Appendix I

211

Appendix J

Box Plot Results

212

Site Group

T101.B1

YUMA

YORKTO

WN

WNY

WHIDBEY ISL

AND

VIRGIN

IA BEA

CH

REM

OTE SE

REM

OTE

S/ SW

REMOTE NC/ NW/ OKLA

HOMA

REM

OTE MW/ NE

REM

OTE

MS/NEW

ORL E

ANS

PORTSM

OUTH-N

H SPYD

PORT HUEN

EME

POINT M

UGU

PHILAD

ELPHIA

PEN

TAGON

PENSA

COLA

PEARL HA

RBOR

PATU

XENT

RIVER

PANAMA CIT

Y

ORLA

NDO

OL D

TOWN

NSY

NORFOL K

NSA

NORFO

LK

NS PO

INT LOMA

NS NORFO

LK

NORT

H ISLAND

NEWPO

RT

NEB

RASKA A

VE.

NAVY

ANNE

X

NAVAL BASE SAN

DIEGO

NAS JR

B FORT WORTH

NAF WASH

INGTON

MILLINGTON

MECH

ANICSB

URG

MCA

S MIRAM

AR

MCAS CH

ERRY PO

INT

MCAS BEA

UFO

RT

MAYPORT

MAKALA

PA

LOS AN

GEL

ES

LITT

LE CREE

K

LEMOORE

LAKEH

URST

KINGS BAY

KANEO

HE BAY

JACK

SONVILLE

INDIAN HEA

D

GULF

PORT/

MER

IDIA

N

GROTON

GREAT

LAKES

FALL

ON

DAHLG

REN

CRYSTAL CITY

CRANE

CORPU

S CHRISTI

CHINA LAKE

CHARL

ESTON

-SC

CARD

EROCK

CAMP SM

ITH

CAMP PEN

DLETON

CAMP LEJEUNE

BRUNSW

ICK

BREM

ERTON

BANGOR

ANACOS

TIA

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Boxplot of T101.B1 by Site Group

Figure 82. Box plots for SLA S101.B1 for factor group.

Appendix J

213

Server Farm

T101.B3

WNY

D

SPSC

SMTH

SDNS

SDNI

PRTH

PRLH

PHIL

PAXR

ORL

O

OCE

N

NWOR

NRFK

MUG

UMILL

MEC

HLT

LCLKHR

LEMR

JAXS

FALN

CRAN

CHRL

CHLK

BREM

1.6

1.5

1.4

1.3

1.2

1.1

1.0

0.9

0.8

0.7

Boxplot of T101.B3 by Server Farm

Figure 83. Box plots for SLA S101.B3 for factor server farm.

Appendix J

214

NOC

T103.1.3

SDNIQUANPRLHNRFK

1.57

1.56

1.55

1.54

1.53

Boxplot of T103.1.3 by NOC

Figure 84. Box plots for SLA S103.1.3 for factor NOC.

Appendix J

215

Region

T103.1.3

USMC - W

EST

USMC - E

AST

TIDE

WAT

ER

SOUT

HWES

T

SOUT

HEAS

T

NORT

HWES

T

NORT

HEAS

TNC

R

HAWAII

1.57

1.56

1.55

1.54

1.53

Boxplot of T103.1.3 by Region

Figure 85. Box plots for SLA S103.1.3 for factor region.

Appendix J

216

Site Group

T103.1.3

YUMA

YORKTO

WN

WNY

WHIDBEY ISLAND

VIRGIN

IA BEA

CH

REM

OTE SE

REM

OTE

S/ SW

REMOTE NC/ NW/ OKLA

HOMA

REM

OTE MW/ NE

REM

OTE

MS/NEW

ORLEANS

PORTSM

OUTH-NH SPY

D

PORT H

UENEM

E

POINT M

UGU

PHILAD

ELPHIA

PENTAGON

PENSA

COL A

PEARL HA

RBOR

PATU

XENT

RIVER

PARRIS ISLAND

PANAMA CIT

Y

ORLA

NDO

OLD

TOWN

NSY NORFO

LK

NSA

NORFO

LK

NS PO

INT LOMA

NS NORFO

LK

NORT

H ISLAND

NEWPO

RT

NEB

RASK

A AVE.

NAVY

ANNE

X

NAVAL BASE SAN

DIEGO

NAS JR

B FORT WORTH

NAF WASH

INGTON

MILLINGTON

MECH

ANICSB

URG

MCA

S MIRAM

AR

MCAS CH

ERRY PO

INT

MCAS BEA

UF O

RT

MAYPORT

MAKALA

PA

LOS AN

GELES

LITT

LE CREEK

LEMOORE

LAKEH

URST

KIN

GS BAY

KANEO

HE BAY

JACK

SONVILLE

INDIAN HEA

D

GULFPO

RT/MER

IDIAN

GROTON

GRE

AT LAKE

S

FAL L

ON

DAHLG

REN

CRYSTAL CITY

CRANE

CORPU

S CHRISTI

CHINA LAKE

CHARL

ESTON

-SC

CARD

EROCK

CAMP SM

ITH

CAMP PEN

DLETON

CAMP LEJE

UNE

BRUNSW

ICK

BREM

ERTON

BANGOR

ANACOS

TIA

1.57

1.56

1.55

1.54

1.53

Boxplot of T103.1.3 by Site Group

Figure 86. Box plots for SLA S103.1.3 for factor group.

Appendix J

217

NOC

T103.3.1

SDNIQUANPRLHNRFK

1.57

1.56

1.55

1.54

1.53

1.52

1.51

1.50

Boxplot of T103.3.1 by NOC

Figure 87. Box plots for SLA S103.3.1 for factor NOC.

Appendix J

218

Region

T103.3.1

USMC - W

EST

USMC - E

AST

TIDE

WAT

ER

SOUT

HWES

T

SOUT

HEAS

T

NORT

HWES

T

NORT

HEAS

TNC

R

HAWAII

1.57

1.56

1.55

1.54

1.53

1.52

1.51

1.50

Boxplot of T103.3.1 by Region


Appendix J

219

Site Group

T103.3.1

YUM

A

YORKTOW

NWNY

WHIDBEY ISLAND

VIRGI

NIA BEA

CH

REM

OTE SE

REM

OTE S/ SW

REM

OTE NC/ NW/ OKLA

HOM

A

REM

OTE

MW/ NE

REM

OTE MS/NEW

ORLEANS

POR

TSM

OUT

H-NH SPY

D

PORT H

UENEM

E

POINT M

UGU

PHILAD

ELPH

IA

PENTA

GON

PENSA

COLA

PEAR

L HAR

BOR

PATUX

ENT RIVER

PARRIS ISLAND

PANAMA CIT

Y

ORLA

NDO

OLD TOW

N

NSY N

ORFO

LK

NSA

NORFO

LK

NS POINT LOM

A

NS NORFO

LK

NORTH

ISLAND

NEWPO

RT

NEB

RAS

KA A

VE.

NAVY

ANNE

X

NAVAL BASE SAN

DIEGO

NAS JR

B FO

RT WORTH

NAF WASH

INGTON

MILLING

TON

MEC

HANICSB

URG

MCAS

MIRAMAR

MCA

S CHE

RRY PO

INT

MCAS BE

AUF O

RT

MAYPORT

MAKALA

PA

LOS ANG

ELES

LITTL

E CREEK

LEMOORE

LAK

EHURST

KINGS

BAY

KANEO

HE B

AY

JACKS

ONVILLE

INDIAN HEA

D

GULFPO

RT/M

ERIDIAN

GRO

TON

GREA

T LAKE

S

FAL L

ON

DAHL G

REN

CRYS

TAL CITY

CRANE

CORPU

S CHRISTI

CHINA L A

KE

CHARLEST

ON-SC

CARD

EROCK

CAMP SMITH

CAMP PENDLETON

CAM

P LEJEU

NE

BRU

NSW

ICK

BREM

ERTON

BAN

GOR

ANACOST

IA

1.57

1.56

1.55

1.54

1.53

1.52

1.51

1.50

Boxplot of T103.3.1 by Site Group


Appendix J

220

Site Size

S103.3.2

SL

14

12

10

8

6

4

2

0

Boxplot of S103.3.2 by Site Size

Figure 90. Box plots for SLA S103.3.2 for factor site size.

Appendix J

221

Region

S103.3.2

USMC - W

EST

USMC - E

AST

TIDE

WAT

ER

SOUT

HWES

T

SOUT

HEAS

T

NORT

HWES

T

NORT

HEAS

TNC

R

HAWAII

14

12

10

8

6

4

2

0

Boxplot of S103.3.2 by Region


Appendix J

222

Site Group

S103.3.2

YUMA

YORKTO

WN

WNY

WHIDBEY ISL

AND

VIRGINIA B

EACH

REM

OTE SE

REM

OTE

S/ SW

REMOTE NC/ NW/ OKLA

HOMA

REM

OTE MW/ NE

PORTSM

OUTH-NH SPYD

PORT HUEN

EME

POINT M

UGU

PHILAD

ELPHI

A

PEN

TAGON

PENSACOLA

PEARL HA

RBOR

PATU

XENT

RIV

ER

PANAMA C

ITY

ORLA

NDO

OLD

TOWN

NSY NORFO

LK

NSA

NORFO

LK

NS POIN

T LOMA

NS NORFO

LK

NORT

H ISLAND

NEWPORT

NEB

RASK

A AVE.

NAVY

ANNE

X

NAVAL BASE SAN

DIEGO

NAS JR

B FORT WORTH

NAF WASH

INGTON

MILLINGTON

MECH

ANIC

SBURG

MCA

S MIRAM

AR

MCAS CH

ERRY PO

INT

MCAS BEAUFO

RT

MAYP

ORT

MAKALA

PA

LOS AN

GEL

ES

L ITT

LE CREEK

LEMOORE

LAKEH

URST

KIN

GS BAY

KANEO

HE BAY

J ACK

SONVILLE

INDIAN HEA

D

GULFPO

RT/MER

IDIAN

GROTON

GRE

AT LAKE

S

FALLON

DAHLG

REN

CRYSTAL CITY

CRANE

CORPU

S CHRISTI

CHINA LAKE

CHARL

ESTON

-SC

CARD

EROCK

CAMP SM

ITH

CAMP PEN

DLETON

BRUNSW

ICK

BREM

ERTON

BANGOR

ANACOS

TIA

14

12

10

8

6

4

2

0

Boxplot of S103.3.2 by Site Group


Appendix J

223

Server Farm

S103.3.2

WNY

D

SPSC

SMTH

SDNS

SDNI

PRTH

PRLH

PHIL

PAXR

ORL

O

OCE

N

NWOR

NRFK

MUG

UMILL

MEC

HLTLC

LKHR

LEMR

JAXS

FALN

CRAN

CHRL

CHLK

BREM

14

12

10

8

6

4

2

0

Boxplot of S103.3.2 by Server Farm

Figure 93. Box plots for SLA S103.3.2 for factor server farm.

Appendix J

224

NOC

T103.4

SDNIQUANPRLHNRFK

1.57

1.56

1.55

1.54

1.53

Boxplot of T103.4 by NOC

Figure 94. Box plots for SLA S103.4 for factor NOC.

Appendix J

225

Region

T103.4

USMC - W

EST

USMC - E

AST

TIDE

WAT

ER

SOUT

HWES

T

SOUT

HEAS

T

NORT

HWES

T

NORT

HEAS

TNC

R

HAWAII

1.57

1.56

1.55

1.54

1.53

Boxplot of T103.4 by Region

Figure 95. Box plots for SLA S103.4 for factor region.

Appendix J

226

Site Group

T103.4

YUM

A

YORKTOW

NWNY

WHIDBEY ISLAND

VIRGI

NIA BEA

CH

REM

OTE SE

REM

OTE S/ SW

REM

OTE NC/ NW/ OKLA

HOM

A

REM

OTE

MW/ NE

REM

OTE MS/NEW

ORLEANS

POR

TSM

OUT

H-NH SPY

D

PORT H

UENEM

E

POINT M

UGU

PHILAD

ELPH

IA

PENTA

GON

PENSA

COLA

PEAR

L HAR

BOR

PATUX

ENT RIVER

PARRIS ISLAND

PANAMA CIT

Y

ORLA

NDO

OLD TOW

N

NSY N

ORFO

LK

NSA

NORFO

LK

NS POINT LOM

A

NS NORFO

LK

NORTH

ISLAND

NEWPO

RT

NEB

RAS

KA A

VE.

NAVY

ANNE

X

NAVAL BASE SAN

DIEGO

NAS JR

B FO

RT WORTH

NAF WASH

INGTON

MILLING

TON

MEC

HANICSB

URG

MCAS

MIRAMAR

MCA

S CHE

RRY PO

INT

MCAS BE

AUF O

RT

MAYPORT

MAKALA

PA

LOS ANG

ELES

LITTL

E CREEK

LEMOORE

LAK

EHURST

KINGS

BAY

KANEO

HE B

AY

JACKS

ONVILLE

INDIAN HEA

D

GULFPO

RT/M

ERIDIAN

GRO

TON

GREA

T LAKE

S

FAL L

ON

DAHL G

REN

CRYS

TAL CITY

CRANE

CORPU

S CHRISTI

CHINA L A

KE

CHARLEST

ON-SC

CARD

EROCK

CAMP SMITH

CAMP PENDLETON

CAM

P LEJEU

NE

BRU

NSW

ICK

BREM

ERTON

BAN

GOR

ANACOST

IA

1.57

1.56

1.55

1.54

1.53

Boxplot of T103.4 by Site Group

Figure 96. Box plots for SLA S103.4 for factor group.

.

Appendix J

227

Site Group

T103.6.B1

YORK

TOWN

WNY

WHIDBE

Y ISLA

ND

VIRG

INIA

BEA

CH

REM

OTE

SE

REM

OTE

S/ SW

REM

OTE NC/ NW/ OKLA

HOMA

REMOTE

MW/ NE

REMOTE

MS/NEW

ORL

EANS

PORT

SMOUTH

-NH SPY

D

PORT HUEN

EME

POIN

T M

UGU

PHILADEL

PHIA

PENTA

GON

PENSA

COLA

PEARL

HARB

OR

PATU

XENT RIVE

R

PANAMA CITY

ORLANDO

OLD

TOWN

NSY

NORFO

LK

NSA NORFO

LK

NS POIN

T LO

MA

NS NORFO

LK

NORTH

ISL

AND

NEW

PORT

NEB

RASK

A AVE

.

NAVY ANNEX

NAVA

L BA

SE SAN DIEGO

NAS JRB FORT

WORT

H

NAF WASH

INGT

ON

MILLINGT

ON

MEC

HANIC

SBURG

MCA

S M

IRAM

AR

MCAS CHERR

Y POIN

T

MAYP

ORT

MAK

ALA

PA

LOS ANGEL

ES

LITT

LE CREE

K

LEMOORE

LAKE

HURS

T

KINGS BA

Y

KANEO

HE BA

Y

JACKS

ONVILLE

INDIA

N HEA

D

GULFPORT/MERIDIAN

GRO

TON

GREA

T LA

KES

FALLON

DAH

LGRE

N

CRYS

TAL CITY

CRA

NE

CORP

US CHRIST

I

CHIN

A LAKE

CHARL

ESTON-S

C

CARD

EROCK

CAMP SMITH

CAMP PEN

DLE

TON

CAMP LE

JEUNE

BRUNSW

ICK

BREM

ERTON

BANG

OR

ANAC

OST

IA

1.50

1.25

1.00

0.75

0.50

Boxplot of T103.6.B1 by Site Group

Figure 97. Box plots for SLA S103.6.B1 for factor group.

Appendix J

228

Region

T103.6.B3

USMC - W

EST

USMC - E

AST

TIDE

WAT

ER

SOUT

HWES

T

SOUT

HEAS

T

NORT

HWES

T

NORT

HEAS

TNC

R

HAWAII

1.6

1.5

1.4

1.3

1.2

1.1

1.0

0.9

Boxplot of T103.6.B3 by Region

Figure 98. Box plots for SLA S103.6.B3 for factor region.

Appendix J

229

Site Group

T103.6.B3

YORKTOW

NWNY

WHIDBEY ISLAND

VIRGINIA B

EACH

REM

OTE SE

REM

OTE S/ SW

REM

OTE NC/ NW/ OKLA

HOM

A

REM

OTE

MW/ NE

REM

OTE MS/NEW

ORLEANS

POR

TSM

OUT

H-NH SPYD

PORT HUEN

EME

POIN

T MUGU

PHILAD

ELPH

IA

PENTA

GON

PENSA

COLA

PEAR

L HAR

BOR

PATUXEN

T RIV

ER

PANAMA C

ITY

ORLA

NDO

OLD T

OWN

NSY NORFO

LK

NSA

NORF O

L K

NS POINT LOM

A

NS NORFO

LK

NORTH

ISLAND

NEWPO

RT

NEB

RAS

KA A

VE.

NAVY

ANNE

X

NAVAL BASE SAN

DIEGO

NAS JR

B FO

RT WORTH

NAF WASH

INGTON

MILLING

TON

MEC

HANIC

SBURG

MCAS

MIRAMAR

MCA

S CHE

RRY PO

INT

MAYP

ORT

MAKALA

PA

L OS ANG

ELES

LITTL

E CREEK

LEMOORE

LAK

EHURST

KINGS

BAY

KANEO

HE B

AY

JACKS

ONVILLE

INDIAN HEA

D

GULFPO

RT/M

ERI

DIAN

GRO

TON

GREA

T LAKE

S

FALLON

DAHLG

REN

CRYSTAL CITY

CRANE

CORPU

S CHRISTI

CHINA L A

KE

CHARL E

STON-

SC

CARD

EROCK

CAMP SMIT

H

CAMP PENDLETON

CAM

P LEJEU

NE

BRU

NSW

ICK

BREM

ERTON

BAN

GOR

ANACOST

IA

1.6

1.5

1.4

1.3

1.2

1.1

1.0

0.9

Boxplot of T103.6.B3 by Site Group

Figure 99. Box plots for SLA S103.6.B3 for factor group.

Appendix J

230

NOC

T105.A

SDNIPRLHNRFK

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Boxplot of T105.A by NOC

Figure 100. Box plots for SLA S105.A for factor NOC.

Appendix J

231

Region

T105.A

USMC - W

EST

USMC - E

AST

TIDE

WAT

ER

SOUT

HWES

T

SOUT

HEAS

T

NORT

HWES

T

NORT

HEAS

TNC

R

HAWAII

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Boxplot of T105.A by Region

Figure 101. Box plots for SLA S105.A for factor region.

Appendix J

232

NOC

T105.B

SDNIPRLHNRFK

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Boxplot of T105.B by NOC

Figure 102. Box plots for SLA S105.B for factor NOC.

Appendix J

233

NOC

T107.2.L3

SDNIPRLHNRFK

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Boxplot of T107.2.L3 by NOC

Figure 103. Box plots for SLA S107.2.L3 for factor NOC.

Appendix J

234

Area

T107.2.L3

WestEast

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Boxplot of T107.2.L3 by Area

Figure 104. Box plots for SLA S107.2.L3 for factor area.

Appendix J

235

Server Farm

T107.2.L3

WNY

D

SPSC

SDNS

SDNI

PRLH

PHIL

ORL

O

OCE

N

NWOR

NRFK

MUG

UMILL

MEC

HLT

LC

LEMR

JAXS

FALN

CRAN

CHRL

CHLK

BREM

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Boxplot of T107.2.L3 by Server Farm

Figure 105. Box plots for SLA S107.2.L3 for factor server farm.

Appendix J

236

Site Group

T107.2.PL1

YUMA

YORK

TOWN

WNY

WHIDBE

Y ISLAND

VIRG

INIA BEA

CH

REMO

TE SE

REMO

TE S/ S

W

REMOT

E NC/

NW/ OK

LAHOM

A

REMO

TE M

W/ N

E

REMOTE

MS/NE

W ORLEA

NS

PORTSM

OUTH

-NH SPY

D

PORT

HUEN

EME

POI N

T MUG

U

PHI L

ADELPH

IA

PENTA

GON

PENSA

COLA

PEAR

L HAR

BOR

PATU

XENT RIVE

R

PARR

IS ISL

AND

PANAM

A CITY

ORLAN

DO

OLD TOWN

NSY NOR

FOLK

NSA NOR

FOLK

NS PO

INT LO

MA

NS NORF

OLK

NORT

H ISLAND

NEWPO

RT

NEBR

ASKA

AVE

.

NAVY

ANNEX

NAV

AL BAS

E SAN D

IEGO

NAS

JRB

FORT

WORT

H

NAF

WAS

HINGTO

N

MILLINGTO

N

MECH

ANIC

SBURG

MCAS MIR

AMAR

MCA

S CH

ERRY

POIN

T

MCAS BE

AUFO

RT

MAYPO

RT

MAKA

LAPA

LOS AN

GELES

LITT

LE CRE

EK

LEMOO

RE

LAKE

HURS

T

KING

S BA

Y

KANEO

HE BA

Y

JACK

SONVI

LLE

INDIA

N HE

AD

GULFPO

RT/M

ERID

IAN

GROTO

N

GREAT

LAKE

S

FALLON

DAHLG

REN

CRYS

TAL CITY

CRAN

E

CORP

US CH

RIST

I

CHIN

A LAKE

CHAR

LEST

ON-SC

CARD

EROCK

CAMP

SMIT

H

CAMP PE

NDLETO

N

CAMP

LEJEUN

E

BRUN

SWICK

BREM

ERTO

N

BANGO

R

ANAC

OST

IA

1.60

1.55

1.50

1.45

1.40

1.35

1.30

Boxplot of T107.2.PL1 by Site Group

Figure 106. Box plots for SLA S107.2.PL1 for factor group.

Appendix J

237

Site Size

T107.3.B

SL

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Boxplot of T107.3.B by Site Size

Figure 107. Box plots for SLA S107.3.B for factor site size.

Appendix J

238

NOC

T107.3.B

SDNIPRLHNRFK

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Boxplot of T107.3.B by NOC

Figure 108. Box plots for SLA S107.3.B for factor NOC.

Appendix J

239

Site Group

T107.3.B

WNY

WHIDB

EY IS

LAND

REMOT

E SE

REMOT

E S/ SW

REMOT

E NC

/ NW

/ OKL

AHOM

A

REMOT

E MW/ N

E

REMOT

E MS/NE

W ORL

EANS

POINT MUG

U

PHILAD

ELPH

IA

PENS

ACOL

A

ORL

ANDO

NEWPO

RT

MCA

S MIRAM

AR

LOS AN

GELE

S

LITT

LE CRE

EK

LAKE

HURS

T

GREA

T LA

KES

CRAN

E

CORP

US CHR

ISTI

BREM

ERTO

N

BANG

OR

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Figure 109. Box plots for SLA S107.3.B for factor group.

.

Appendix J

240

Server Farm

T107.3.B

WNY

D

SPSC

SDNI

PRTH

PHIL

NWOR

MUG

UMILL

MEC

HLT

LCLKHR

JAXS

FALN

CRAN

CHRL

CHLK

BREM

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Figure 110. Box plots for SLA S107.3.B for factor server farm.

Appendix J

Documents

A causal comparative factorial analysis of factors