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11
© 2008 Thomson South-Western. All Rights Reserved© 2008 Thomson South-Western. All Rights Reserved
Slides by
JOHNLOUCKSSt. Edward’sUniversity
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© 2008 Thomson South-Western. All Rights Reserved© 2008 Thomson South-Western. All Rights Reserved
Chapter 1Chapter 1
Data and StatisticsData and Statistics
I needI need
help!help!s Applications in Business and Economics
s Data
s Data Sources
s Descriptive Statistics
s Statistical Inference
s Computers and
Statistical Analysis
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© 2008 Thomson South-Western. All Rights Reserved© 2008 Thomson South-Western. All Rights Reserved
Applications inApplications in
Business and EconomicsBusiness and Economics
s
AccountingAccounting
s EconomicsEconomics
Public accounting firms use statisticalPublic accounting firms use statistical
sampling procedures when conductingsampling procedures when conducting
audits for their clients.audits for their clients.
Economists use statistical informationEconomists use statistical information
in making forecasts about the future of in making forecasts about the future of
the economy or some aspect of it.the economy or some aspect of it.
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Applications inApplications in
Business and EconomicsBusiness and Economics
A variety of statistical qualityA variety of statistical quality
control charts are used to monitorcontrol charts are used to monitor
the output of a production process.the output of a production process.
s ProductionProduction
Electronic point-of-sale scanners atElectronic point-of-sale scanners at
retail checkout counters are used toretail checkout counters are used to
collect data for a variety of marketingcollect data for a variety of marketing
research applications.research applications.
s
MarketingMarketing
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55 © 2008 Thomson South-Western. All Rights Reserved© 2008 Thomson South-Western. All Rights Reserved
Applications inApplications in
Business and EconomicsBusiness and Economics
Financial advisors use price-earnings ratios andFinancial advisors use price-earnings ratios and
dividend yields to guide their investmentdividend yields to guide their investment
recommendations.recommendations.
FinanceFinance
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66 © 2008 Thomson South-Western. All Rights Reserved© 2008 Thomson South-Western. All Rights Reserved
Data and Data SetsData and Data Sets
s DataData are the facts and figures collected, summarized,are the facts and figures collected, summarized,
analyzed, and interpreted.analyzed, and interpreted.
The data collected in a particular study are referred The data collected in a particular study are referred
to as theto as the data setdata set..
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The The elementselements are the entities on which data areare the entities on which data arecollected.collected.
AA variablevariable is a characteristic of interest for the elements.is a characteristic of interest for the elements.
The set of measurements collected for a particular The set of measurements collected for a particular
element is called anelement is called an observationobservation..
The total number of data values in a complete data The total number of data values in a complete data
set is the number of elements multiplied by theset is the number of elements multiplied by the
number of variables.number of variables.
Elements, Variables, and ObservationsElements, Variables, and Observations
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Stock Annual Earn/Stock Annual Earn/
Exchange Sales($M) Share($)Exchange Sales($M) Share($)
Data, Data Sets,Data, Data Sets,
Elements, Variables, and ObservationsElements, Variables, and Observations
CompanyCompany
DataramDataram
EnergySouthEnergySouth
KeystoneKeystone
LandCareLandCarePsychemedicsPsychemedics
NQNQ 73.1073.10 0.860.86
NN 74.0074.00 1.671.67
NN 365.70365.70 0.860.86
NQNQ 111.40111.40 0.330.33NN 17.6017.60 0.130.13
VariableVariable
ssElemenElementt
NamesNames
Data SetData Set
ObservatioObservatio
nn
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Scales of MeasurementScales of Measurement
The scale indicates the data summarization and The scale indicates the data summarization andstatistical analyses that are most appropriate.statistical analyses that are most appropriate.
The scale indicates the data summarization and The scale indicates the data summarization andstatistical analyses that are most appropriate.statistical analyses that are most appropriate.
The scale determines the amount of information The scale determines the amount of information
contained in the data.contained in the data.
The scale determines the amount of information The scale determines the amount of information
contained in the data.contained in the data.
Scales of measurement include:Scales of measurement include:Scales of measurement include:Scales of measurement include:NominalNominal
OrdinalOrdinal
IntervalInterval
RatioRatio
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Scales of MeasurementScales of Measurement
s NominalNominal
AA nonnumeric labelnonnumeric label oror numeric codenumeric code may be used.may be used.AA nonnumeric labelnonnumeric label oror numeric codenumeric code may be used.may be used.
Data areData are labels or nameslabels or names used to identify anused to identify an
attribute of the element.attribute of the element.
Data areData are labels or nameslabels or names used to identify anused to identify an
attribute of the element.attribute of the element.
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Example:Example:
Students of a university are classified by theStudents of a university are classified by the
school in which they are enrolled using aschool in which they are enrolled using a
nonnumeric label such as Business, Humanities,nonnumeric label such as Business, Humanities,Education, and so on.Education, and so on.
Alternatively, a numeric code could be used forAlternatively, a numeric code could be used for
the school variable (e.g. 1 denotes Business,the school variable (e.g. 1 denotes Business,
2 denotes Humanities, 3 denotes Education, and2 denotes Humanities, 3 denotes Education, andso on).so on).
Example:Example:
Students of a university are classified by theStudents of a university are classified by the
school in which they are enrolled using aschool in which they are enrolled using a
nonnumeric label such as Business, Humanities,nonnumeric label such as Business, Humanities,Education, and so on.Education, and so on.
Alternatively, a numeric code could be used forAlternatively, a numeric code could be used for
the school variable (e.g. 1 denotes Business,the school variable (e.g. 1 denotes Business,
2 denotes Humanities, 3 denotes Education, and2 denotes Humanities, 3 denotes Education, andso on).so on).
Scales of MeasurementScales of Measurement
s NominalNominal
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Scales of MeasurementScales of Measurement
s OrdinalOrdinal
AA nonnumeric labelnonnumeric label oror numeric codenumeric code may be used.may be used.AA nonnumeric labelnonnumeric label oror numeric codenumeric code may be used.may be used.
The data have the properties of nominal data and The data have the properties of nominal data and
thethe order or rank of the data is meaningfulorder or rank of the data is meaningful..
The data have the properties of nominal data and The data have the properties of nominal data and
thethe order or rank of the data is meaningfulorder or rank of the data is meaningful..
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Scales of MeasurementScales of Measurement
s OrdinalOrdinal
Example:Example:
Students of a university are classified by theirStudents of a university are classified by their
class standing using a nonnumeric label such asclass standing using a nonnumeric label such as
Freshman, Sophomore, Junior, or Senior.Freshman, Sophomore, Junior, or Senior.
Alternatively, a numeric code could be used forAlternatively, a numeric code could be used for
the class standing variable (e.g. 1 denotesthe class standing variable (e.g. 1 denotes
Freshman, 2 denotes Sophomore, and so on).Freshman, 2 denotes Sophomore, and so on).
Example:Example:
Students of a university are classified by theirStudents of a university are classified by their
class standing using a nonnumeric label such asclass standing using a nonnumeric label such as
Freshman, Sophomore, Junior, or Senior.Freshman, Sophomore, Junior, or Senior.
Alternatively, a numeric code could be used forAlternatively, a numeric code could be used for
the class standing variable (e.g. 1 denotesthe class standing variable (e.g. 1 denotes
Freshman, 2 denotes Sophomore, and so on).Freshman, 2 denotes Sophomore, and so on).
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Scales of MeasurementScales of Measurement
s IntervalInterval
Interval data areInterval data are always numericalways numeric..Interval data areInterval data are always numericalways numeric..
The data have the properties of ordinal data, and The data have the properties of ordinal data, and
the interval between observations is expressed inthe interval between observations is expressed in
terms of a fixed unit of measure.terms of a fixed unit of measure.
The data have the properties of ordinal data, and The data have the properties of ordinal data, and
the interval between observations is expressed inthe interval between observations is expressed in
terms of a fixed unit of measure.terms of a fixed unit of measure.
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Scales of MeasurementScales of Measurement
s IntervalInterval
Example:Example:
Melissa has an SAT score of 1205, while KevinMelissa has an SAT score of 1205, while Kevin
has an SAT score of 1090. Melissa scored 115has an SAT score of 1090. Melissa scored 115
points more than Kevin.points more than Kevin.
Example:Example:
Melissa has an SAT score of 1205, while KevinMelissa has an SAT score of 1205, while Kevin
has an SAT score of 1090. Melissa scored 115has an SAT score of 1090. Melissa scored 115
points more than Kevin.points more than Kevin.
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Scales of MeasurementScales of Measurement
s RatioRatio
The data have all the properties of interval data The data have all the properties of interval data
and theand the ratio of two values is meaningfulratio of two values is meaningful..
The data have all the properties of interval data The data have all the properties of interval data
and theand the ratio of two values is meaningfulratio of two values is meaningful..
Variables such as distance, height, weight, and timeVariables such as distance, height, weight, and time
use the ratio scale.use the ratio scale.Variables such as distance, height, weight, and timeVariables such as distance, height, weight, and timeuse the ratio scale.use the ratio scale.
This This scale must contain a zero valuescale must contain a zero value that indicatesthat indicates
that nothing exists for the variable at the zero point.that nothing exists for the variable at the zero point.
This This scale must contain a zero valuescale must contain a zero value that indicatesthat indicates
that nothing exists for the variable at the zero point.that nothing exists for the variable at the zero point.
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Scales of MeasurementScales of Measurement
s RatioRatio
Example:Example:
Melissa’s college record shows 36 credit hoursMelissa’s college record shows 36 credit hours
earned, while Kevin’s record shows 72 creditearned, while Kevin’s record shows 72 credit
hours earned. Kevin has twice as many credithours earned. Kevin has twice as many credithours earned as Melissa.hours earned as Melissa.
Example:Example:
Melissa’s college record shows 36 credit hoursMelissa’s college record shows 36 credit hours
earned, while Kevin’s record shows 72 creditearned, while Kevin’s record shows 72 credit
hours earned. Kevin has twice as many credithours earned. Kevin has twice as many credithours earned as Melissa.hours earned as Melissa.
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Data can be further classified as being qualitativeData can be further classified as being qualitativeor quantitative.or quantitative.
Data can be further classified as being qualitativeData can be further classified as being qualitativeor quantitative.or quantitative.
The statistical analysis that is appropriate depends The statistical analysis that is appropriate depends
on whether the data for the variable are qualitativeon whether the data for the variable are qualitativeor quantitative.or quantitative.
The statistical analysis that is appropriate depends The statistical analysis that is appropriate depends
on whether the data for the variable are qualitativeon whether the data for the variable are qualitativeor quantitative.or quantitative.
In general, there are more alternatives for statisticalIn general, there are more alternatives for statistical
analysis when the data are quantitative.analysis when the data are quantitative.
In general, there are more alternatives for statisticalIn general, there are more alternatives for statistical
analysis when the data are quantitative.analysis when the data are quantitative.
Qualitative and Quantitative DataQualitative and Quantitative Data
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Qualitative DataQualitative Data
Labels or namesLabels or names used to identify an attribute of eachused to identify an attribute of eachelementelementLabels or namesLabels or names used to identify an attribute of eachused to identify an attribute of eachelementelement
Often referred to asOften referred to as categorical datacategorical dataOften referred to asOften referred to as categorical datacategorical data
Use either the nominal or ordinal scale of Use either the nominal or ordinal scale of
measurementmeasurement
Use either the nominal or ordinal scale of Use either the nominal or ordinal scale of
measurementmeasurement
Can be either numeric or nonnumericCan be either numeric or nonnumericCan be either numeric or nonnumericCan be either numeric or nonnumeric
Appropriate statistical analyses are rather limitedAppropriate statistical analyses are rather limitedAppropriate statistical analyses are rather limitedAppropriate statistical analyses are rather limited
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Quantitative DataQuantitative Data
Quantitative data indicateQuantitative data indicate how many or how much:how many or how much:Quantitative data indicateQuantitative data indicate how many or how much:how many or how much:
discretediscrete, if measuring how many, if measuring how manydiscretediscrete, if measuring how many, if measuring how many
continuouscontinuous, if measuring how much, if measuring how muchcontinuouscontinuous, if measuring how much, if measuring how much
Quantitative data areQuantitative data are always numericalways numeric..Quantitative data areQuantitative data are always numericalways numeric..
Ordinary arithmetic operations are meaningful forOrdinary arithmetic operations are meaningful forquantitative data.quantitative data.
Ordinary arithmetic operations are meaningful forOrdinary arithmetic operations are meaningful forquantitative data.quantitative data.
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Scales of MeasurementScales of Measurement
QualitativeQualitativeQualitativeQualitative QuantitativQuantitativ
eeQuantitativQuantitativ
ee
NumericalNumericalNumericalNumerical NumericalNumericalNumericalNumericalNon-Non-
numericalnumericalNon-Non-
numericalnumerical
DataDataDataData
NominaNominallNominaNominall
OrdinaOrdinallOrdinaOrdinall
NominalNominalNominalNominal OrdinalOrdinalOrdinalOrdinal IntervalIntervalIntervalInterval RatioRatioRatioRatio
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2222 © 2008 Thomson South-Western. All Rights Reserved© 2008 Thomson South-Western. All Rights Reserved
Cross-Sectional DataCross-Sectional Data
Cross-sectional dataCross-sectional data are collected at the same orare collected at the same orapproximately the same point in time.approximately the same point in time. Cross-sectional dataCross-sectional data are collected at the same orare collected at the same orapproximately the same point in time.approximately the same point in time.
ExampleExample: data detailing the number of building: data detailing the number of building
permits issued in June 2007 in each of the countiespermits issued in June 2007 in each of the countiesof Ohioof Ohio
ExampleExample: data detailing the number of building: data detailing the number of building
permits issued in June 2007 in each of the countiespermits issued in June 2007 in each of the counties
of Ohioof Ohio
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Time Series Data Time Series Data
Time series data Time series data are collected over several timeare collected over several timeperiods.periods.
Time series data Time series data are collected over several timeare collected over several timeperiods.periods.
ExampleExample: data detailing the number of building: data detailing the number of building
permits issued in Lucas County, Ohio in each of permits issued in Lucas County, Ohio in each of the last 36 monthsthe last 36 months
ExampleExample: data detailing the number of building: data detailing the number of building
permits issued in Lucas County, Ohio in each of permits issued in Lucas County, Ohio in each of
the last 36 monthsthe last 36 months
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Data SourcesData Sources
s Existing SourcesExisting Sources
Within a firmWithin a firm – almost any department– almost any department
Business database servicesBusiness database services – Dow Jones & Co.– Dow Jones & Co.
Government agenciesGovernment agencies - U.S. Department of Labor- U.S. Department of Labor
Industry associationsIndustry associations – Travel Industry Association– Travel Industry Association
of Americaof America
Special-interest organizationsSpecial-interest organizations – Graduate Management– Graduate Management
Admission CouncilAdmission Council
InternetInternet – more and more firms– more and more firms
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2525 © 2008 Thomson South-Western. All Rights Reserved© 2008 Thomson South-Western. All Rights Reserved
s Statistical StudiesStatistical Studies
Data SourcesData Sources
InIn experimental studiesexperimental studies the variable of interest isthe variable of interest is
first identified. Then one or more other variablesfirst identified. Then one or more other variables
are identified and controlled so that data can beare identified and controlled so that data can be
obtained about how they influence the variable of obtained about how they influence the variable of interest.interest.
InIn experimental studiesexperimental studies the variable of interest isthe variable of interest is
first identified. Then one or more other variablesfirst identified. Then one or more other variables
are identified and controlled so that data can beare identified and controlled so that data can be
obtained about how they influence the variable of obtained about how they influence the variable of interest.interest.
InIn observationalobservational (nonexperimental)(nonexperimental) studiesstudies nono
attempt is made to control or influence theattempt is made to control or influence thevariables of interest.variables of interest.
InIn observationalobservational (nonexperimental)(nonexperimental) studiesstudies nono
attempt is made to control or influence theattempt is made to control or influence thevariables of interest.variables of interest. aa surveysurvey is a goodis a good
exampleexampleaa surveysurvey is a goodis a good
exampleexample
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Data Acquisition ConsiderationsData Acquisition Considerations
Time Requirement Time Requirement Time Requirement Time Requirement
Cost of AcquisitionCost of AcquisitionCost of AcquisitionCost of Acquisition
Data ErrorsData ErrorsData ErrorsData Errors
• Searching for information can be time consuming.Searching for information can be time consuming.
• Information may no longer be useful by the time itInformation may no longer be useful by the time itis available.is available.
• Organizations often charge for information evenOrganizations often charge for information evenwhen it is not their primary business activity.when it is not their primary business activity.
• Using any data that happen to be available or wereUsing any data that happen to be available or were
acquired with little care can lead to misleadingacquired with little care can lead to misleading
information.information.
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Descriptive StatisticsDescriptive Statistics
s Descriptive statisticsDescriptive statistics are the tabular,are the tabular,
graphical, and numerical methods used tographical, and numerical methods used tosummarize and presentsummarize and present data.data.
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2828 © 2008 Thomson South-Western. All Rights Reserved© 2008 Thomson South-Western. All Rights Reserved
Example: Hudson Auto RepairExample: Hudson Auto Repair
The manager of Hudson Auto The manager of Hudson Auto
would like to have a betterwould like to have a better
understanding of the costunderstanding of the cost
of parts used in the engineof parts used in the engine
tune-ups performed in thetune-ups performed in theshop. She examines 50shop. She examines 50
customer invoices for tune-ups. The costs of customer invoices for tune-ups. The costs of
parts,parts,
rounded to the nearest dollar, are listed on therounded to the nearest dollar, are listed on thenextnext
slide.slide.
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Example: Hudson Auto RepairExample: Hudson Auto Repair
s Sample of Parts Cost ($) for 50 Tune-Sample of Parts Cost ($) for 50 Tune-
upsups91 78 93 57 75 52 99 80 97 62
71 69 72 89 66 75 79 75 72 76
104 74 62 68 97 105 77 65 80 109
85 97 88 68 83 68 71 69 67 7462 82 98 101 79 105 79 69 62 73
91 78 93 57 75 52 99 80 97 62
71 69 72 89 66 75 79 75 72 76
104 74 62 68 97 105 77 65 80 109
85 97 88 68 83 68 71 69 67 7462 82 98 101 79 105 79 69 62 73
b lT b l S
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Tabular Summary: Tabular Summary:
Frequency and Percent FrequencyFrequency and Percent Frequency
50-5950-59
60-6960-69
70-7970-79
80-8980-89
90-9990-99
100-109100-109
22
1313
1616
77
77
555050
44
2626
3232
1414
1414
1010100100
(2/50)10(2/50)1000
(2/50)10(2/50)1000
PartsPartsCost ($)Cost ($)
PartsPartsFrequencyFrequency
PercentPercentFrequencyFrequency
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Graphical Summary: HistogramGraphical Summary: Histogram
2244
66
88
1010
1212
1414
1616
1818
PartsCost ($)
PartsCost ($)
F r e q u e
n c y
F r e q u e
n c y
50−59 60−69 70−79 80−89 90−99 100-11050−59 60−69 70−79 80−89 90−99 100-110
Tune-up Parts Cost Tune-up Parts Cost Tune-up Parts Cost Tune-up Parts Cost
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Numerical Descriptive StatisticsNumerical Descriptive Statistics
Hudson’s average cost of parts, based on the 50Hudson’s average cost of parts, based on the 50
tune-ups studied, is $79 (found by summing thetune-ups studied, is $79 (found by summing the
50 cost values and then dividing by 50).50 cost values and then dividing by 50).
The most common numerical descriptive statistic The most common numerical descriptive statistic
is theis the averageaverage (or(or meanmean).).
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Statistical InferenceStatistical Inference
PopulationPopulationPopulationPopulation
SampleSampleSampleSample
Statistical inferenceStatistical inferenceStatistical inferenceStatistical inference
CensusCensusCensusCensus
Sample surveySample surveySample surveySample survey
−− the set of all elements of interest in athe set of all elements of interest in a
particular studyparticular study
−−a subset of the populationa subset of the population
−− the process of using data obtainedthe process of using data obtained
from a sample to make estimatesfrom a sample to make estimatesand test hypotheses about theand test hypotheses about the
characteristics of a populationcharacteristics of a population
−−collecting data for a populationcollecting data for a population
−−collecting data for a samplecollecting data for a sample
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Process of Statistical InferenceProcess of Statistical Inference
11. Population. Populationconsists of all tune-consists of all tune-
ups. Average cost of ups. Average cost of parts is unknownparts is unknown.
11. Population. Populationconsists of all tune-consists of all tune-
ups. Average cost of ups. Average cost of parts is unknownparts is unknown.
22. A sample of 50. A sample of 50
engine tune-upsengine tune-ups
is examined.is examined.
22. A sample of 50. A sample of 50
engine tune-upsengine tune-ups
is examined.is examined.
3.3. The sample data The sample dataprovide a sampleprovide a sample
average parts costaverage parts costof $79 per tune-up.of $79 per tune-up.
3.3. The sample data The sample dataprovide a sampleprovide a sample
average parts costaverage parts costof $79 per tune-up.of $79 per tune-up.
44. The sample average. The sample average
is used to estimate theis used to estimate the
population average.population average.
44. The sample average. The sample average
is used to estimate theis used to estimate the
population average.population average.
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Computers and Statistical AnalysisComputers and Statistical Analysis
Statistical analysis typically involves working withStatistical analysis typically involves working with
large amounts of datalarge amounts of data.. Computer softwareComputer software is typically used to conduct theis typically used to conduct the
analysis.analysis.
Instructions are provided in chapter appendices forInstructions are provided in chapter appendices for
carrying out many of the statistical procedurescarrying out many of the statistical proceduresusing Minitab and Excel.using Minitab and Excel.