The Where, The Where, WhyWhy, and , and How of Data CollectionHow of Data Collection

©

Business StatisticsBusiness Statistics

Business statistics Business statistics consists of a set of tools consists of a set of tools and techniques that are and techniques that are used to convert data into used to convert data into meaningful information meaningful information for a business for a business environment.environment.

Objective in Business Objective in Business StatisticsStatistics

Descriptive StatisticsDescriptive StatisticsDescribe

Compare

RelateInferential StatisticsInferential Statistics

Descriptive StatisticsDescriptive Statistics

Descriptive StatisticsDescriptive Statistics consists of the tools and techniques designed to describe data, such as charts, graphs, and numerical measures.

Descriptive StatisticsDescriptive Statistics(Figure 1-2: Histogram)(Figure 1-2: Histogram)

BAKER CITY HOSPITAL - LENGTH OF STAY DISTRIBUTION

0

10

20

30

40

50

60

70

0<2 2<4 4<6 6<8 8<10 10<12 12<14 14<16 16<18

Descriptive StatisticsDescriptive Statistics

AVERAGEAVERAGEThe sum of all the values divided by the number of values. In equation form:

where:

N = number of data values

xi = ith data value

sdata valuenumber of

es data valusum of all

N

xi

Average

N

1

Inferential StatisticsInferential Statistics

Inferential StatisticsInferential Statistics consists of techniques that allow a decision-maker to reach a conclusion about characteristics of a larger data set based upon a subset of those data

Two Basic Categories of Two Basic Categories of Statistical Inference ToolsStatistical Inference Tools

EstimationEstimationHypothesis TestingHypothesis Testing

Data TypesData Types

Primary DataPrimary DataThose that are collected by you or

another person with whom you are closely associated.

Secondary DataSecondary DataThose that are collected and compiled

by an outside source or by someone in your organization who may later provide access to the data to other users.

Tools for Collecting DataTools for Collecting Data

ExperimentsExperimentsTelephone SurveysTelephone SurveysMail QuestionnairesMail QuestionnairesDirect Observation Direct Observation

and Personal and Personal InterviewInterview

ExperimentsExperiments

An experimentexperiment is any process that generates data as its outcome.

Major Steps for a Major Steps for a Telephone or Written Telephone or Written

SurveySurvey

Define the IssueDefine the Issue Define the Population of InterestDefine the Population of Interest Develop Survey QuestionsDevelop Survey Questions Pre-test the SurveyPre-test the Survey Determine the Sample Size and Sampling Determine the Sample Size and Sampling

MethodMethod Select Sample and AdministerSelect Sample and Administer

SurveysSurveys

• Open ended questionsOpen ended questions

• Closed-ended questionsClosed-ended questions

• Demographic questionsDemographic questions

Populations and SamplesPopulations and Samples

A populationpopulation is a set of specific data values on all objects or individuals of interest.

Populations and SamplesPopulations and Samples

A samplesample is a subset of the population.

Parameters and StatisticsParameters and Statistics

Descriptive numerical measures calculated from the entire population are called parametersparameters.Corresponding measures for a sample are called statisticsstatistics.

Sampling TechniquesSampling Techniques

Non-statistical sampling Non-statistical sampling techniquestechniques refer to those methods of sampling using influence, judgement, or other non-chance processes.Example: Convenience Example: Convenience samplingsampling -- sample from the population based upon accessibility and ease of selection.

Sampling TechniquesSampling Techniques

Statistical sampling Statistical sampling techniquestechniques refer to those methods of sampling that use selection techniques based upon chance selection.

Statistical SamplingStatistical Sampling

Types of statistical sampling include:

Simple Random SamplingSimple Random Sampling Stratified Random SamplingStratified Random Sampling Systematic SamplingSystematic Sampling Cluster SamplingCluster Sampling

Statistical SamplingStatistical Sampling

Simple random samplingSimple random sampling refers to a method of selecting items from a population such that every possible sample of a specified size has an equal chance of being selected.

Statistical SamplingStatistical Sampling

Stratified random samplingStratified random sampling refers to a sampling method in which the population is divided into subgroups called strata so that each population item belongs to only one strata. The objective is to form strata such that the population values of interest are as much alike as possible.

Stratified Sampling ExampleStratified Sampling Example(Figure 1-13)(Figure 1-13)

PopulationPopulation

Stratified PopulationStratified Population

Financial Institutions

Stratified Sampling ExampleStratified Sampling Example(Figure 1-13)(Figure 1-13)

PopulationPopulation

Cash holdings of All Financial

Institutions in the United States

Stratified PopulationStratified Population

Financial Institutions

Stratified Sampling ExampleStratified Sampling Example(Figure 1-13)(Figure 1-13)

PopulationPopulation

Cash holdings of All Financial

Institutions in the United States

Stratified PopulationStratified Population

Financial Institutions

Stratified Sampling ExampleStratified Sampling Example(Figure 1-13)(Figure 1-13)

PopulationPopulation

Cash holdings of All Financial

Institutions in the United States

Large Institutions

Stratified PopulationStratified Population

Stratum 1 Select n1

Financial Institutions

Stratified Sampling ExampleStratified Sampling Example(Figure 1-13)(Figure 1-13)

PopulationPopulation

Cash holdings of All Financial

Institutions in the United States

Large Institutions

Medium Size Institutions

Stratified PopulationStratified Population

Stratum 1

Stratum 2

Select n1

Select n2

Financial Institutions

Stratified Sampling ExampleStratified Sampling Example(Figure 1-13)(Figure 1-13)

PopulationPopulation

Cash holdings of All Financial

Institutions in the United States

Large Institutions

Medium Size Institutions

Small Institutions

Stratified PopulationStratified Population

Stratum 1

Stratum 2

Stratum 3

Select n1

Select n2

Select n3

Financial Institutions

Statistical SamplingStatistical Sampling

Systemic random samplingSystemic random sampling refers to a sampling technique that involves selecting the kth item in the population after randomly selecting a starting point between 1 and k. The value of k is determined as the ratio of the population size over the desired sample size.

Statistical SamplingStatistical Sampling

Cluster samplingCluster sampling refers to a method by which the population is divided into groups, or clusters, that are each intended to be mini-populations. A random sample of m clusters is selected.

Cluster Sampling ExampleCluster Sampling Example(Figure 1-14)(Figure 1-14)

42 22 52

Illinois Scotland Florida

25 105 20 36 152 76 37

Algeria California Alaska New York Idaho Mexico Australia

Mid-Level Managers by Location for Morrison-Knudsen Construction Company

Cluster Sampling ExampleCluster Sampling Example(Figure 1-14)(Figure 1-14)

42 22 52

Illinois Scotland Florida

Mid-Level Managers by Location for Morrison-Knudsen Construction Company

All members selected from these clusters

Quantitative and Quantitative and Qualitative DataQualitative Data

Data that are numeric and which define value or quantity are quantitative quantitative datadata.Data whose measurement scale is inherently categorical are qualitative qualitative datadata.

Time Series Data and Time Series Data and Cross-Sectional DataCross-Sectional Data

Time series dataTime series data consist of a set of ordered data values observed at successive points in time.Cross-sectional dataCross-sectional data are a set of data values observed at a fixed point in time.

Data Measurement LevelsData Measurement Levels

Nominal Nominal DataData

Ordinal Ordinal (Rank) Data(Rank) Data

Interval Interval Data Data

Ratio DataRatio Data

Data Level HierarchyData Level Hierarchy(Figure 1-15)(Figure 1-15)

Ratio/Interval Data

Ordinal Data

Nominal Data

Highest Level

Complete Analysis

Higher Level

Mid-level Analysis

Lowest Level

Basic Analysis

Categorical Codes ID Numbers Category Names

Rankings

Ordered Categories

Measurements