Chapter 1

Introduction and Data CollectionStatistics for ManagersUsing Microsoft Excel 4th Edition

Chapter GoalsAfter completing this chapter, you should be able to: Explain key definitions: Population vs. Sample Primary vs. Secondary Data Parameter vs. Statistic Descriptive vs. Inferential StatisticsDescribe key data collection methodsDescribe different sampling methodsProbability Samples vs. Nonprobability SamplesSelect a random sample by computer generationIdentify types of data and levels of measurementDescribe the different types of survey error

Why a Manager Needs to Know about StatisticsTo know how to: properly present information (describe things)draw conclusions about populations based on sample information (make decisions)improve processesobtain reliable forecasts

Key DefinitionsA population is the collection of all items or things under consideration people or objectsA sample is a portion of the population selected for analysisA parameter is a summary measure that describes a characteristic of the populationA statistic is a summary measure computed from a sample

Population vs. Sample a b c d ef gh i jk l m n o p q rs t u v w x y z

PopulationSample b c g i n o r u y

Measures used to describe the population are called parametersMeasures computed from sample data are called statistics

Key DefinitionsA survey is the gathering of data about a particular group of people or items A census is a survey of the entire population

A sample is a survey of a portion of the population

Two Branches of StatisticsDescriptive statisticsCollecting, summarizing, and describing dataInferential statisticsDrawing conclusions and/or making decisions concerning a population based only on sample data

Descriptive StatisticsCollect datae.g. SurveyPresent datae.g. Tables and graphsCharacterize datae.g. Sample mean =

Inferential StatisticsEstimatione.g.: Estimate the population mean weight using the sample mean weightHypothesis testinge.g.: Test the claim that the population mean weight is over 120 poundsDrawing conclusions and/or making decisions concerning a population based on sample results.

Why We Need DataTo provide input to study a situationTo measure performance of service or production processesTo evaluate conformance to standardsTo assist in formulating alternative courses of actionTo satisfy curiosity

Data SourcesSecondaryData CompilationObservationExperimentationPrint or Electronic SurveyPrimaryData Collection

Types of DataExamples:Marital StatusPolitical PartyEye Color (Defined categories)Examples:Number of ChildrenDefects per hour (Counted items)Examples:WeightVoltage (Measured characteristics)

Levels of Measurementand Measurement ScalesInterval DataOrdinal Data Nominal DataHighest LevelStrongest forms of measurementHigher LevelLowest LevelWeakest form of measurementCategories (no ordering or direction)Ordered Categories (rankings, order, or scaling) Differences between measurements but no true zeroRatio DataDifferences between measurements, true zero exists

Example DataSubject Name Height Income Gender Eye color 1 Mary6210,350FemaleBlue 2 John7230,500Male Brown 3 Jill 6435,600FemaleGreen 4 Donna5920,700FemaleBrown 5 Sam7315,300Male Blue 6 Bill 7052,800Male Black 7 Mario7119,400Male Blue 8 Carol7312,500FemaleBrown 9 Betty7030,200FemaleBrown 10 Linda6822,700FemaleBrown

Data in Frequency Distributions Height Income Category FrequencyCategory Frequency>54 to 60 1 20K4>60 to 66 2>20K to 50K5>66 to 72 5 > 50K 1>72 to 78 2

Gender Eye ColorCategory Frequency Category Frequency Female 6Black 1 Male 4Blue3Brown5Green1

Statistical DataNumerical Data can be gathered as grouped or converted after gathering.Categorical data is by nature always groupedClasses for numerical data are usually a range of valuesClasses for categorical data are usually single valuedNumerical data is usually grouped for graphical presentation

Reasons for Drawing a SampleLess time consuming than a census

Less costly to administer than a census

Types of Samples UsedQuotaSamplesNon-Probability SamplesJudgementChunkProbability SamplesSimple RandomSystematicStratifiedClusterConvenience(continued)

Probability SamplingItems in the sample are chosen based on known probabilitiesProbability SamplesSimple RandomSystematicStratifiedCluster

Simple Random SamplesEvery individual or item from the frame has an equal chance of being selectedSelection may be with replacement or without replacementSamples obtained from computer random number generators

Systematic SamplesDecide on sample size: nDivide frame of N individuals into groups of k individuals: k=N/nRandomly select one individual from the 1st group Select every kth individual thereafterN = 64n = 8k = 8First Group

Stratified SamplesPopulation divided into two or more subgroups (called strata) according to some common characteristicSimple random sample selected from each subgroupSamples from subgroups are combined into onePopulationDividedinto 4strataSample

Cluster SamplesPopulation is divided into clusters, each representative of the populationA simple random sample of clusters is selectedAll items in the selected clusters can be used, or items can be chosen from a cluster using another probability sampling techniquePopulation divided into 16 clusters.Randomly selected clusters for sample

Advantages and DisadvantagesSimple random sample and systematic sampleSimple to useMay not be a good representation of the populations underlying characteristics that have small probabilitiesStratified sampleEnsures representation of individuals across the entire populationCluster sampleMore cost effectiveLess efficient (need larger sample to acquire the same level of precision)

Types of Survey ErrorsCoverage error or selection biasExists if some groups are excluded from the frame and have no chance of being selectedNon response error or biasPeople who do not respond may be different from those who do respondSampling errorVariation from sample to sample will always existMeasurement errorDue to weaknesses in question design, respondent error, and interviewers effects on the respondent

Evaluating Survey WorthinessWhat is the purpose of the survey?Is the survey based on a probability sample?Are there coverage errors (appropriate frame)?Is there Non-response error (follow up)Is there Measurement error (good questions elicit good responses)Is the sampling error acceptable (always exists)

Chapter SummaryReviewed why a manager needs to know statisticsIntroduced key definitionsExamined descriptive vs. inferential statisticsDescribed different types of samplesReviewed data types and measurement levelsExamined survey worthiness and types of survey errors