戴維商務統(tǒng)計學第7版英文版教學課件BSFC7e-CH01

1、Defining and Collecting DataChapter 1Defining and Collecting DataChObjectivesIn this chapter you learn: To understand issues that arise when defining variables.How to define variablesHow to collect dataTo identify different ways to collect a sampleUnderstand the types of survey errorsObjectivesIn th

2、is chapter you Classifying Variables By TypeCategorical (qualitative) variables take categories as their values such as “yes”, “no”, or “blue”, “brown”, “green”. Numerical (quantitative) variables have values that represent a counted or measured quantity.Discrete variables arise from a counting proc

3、essContinuous variables arise from a measuring process DCOVAClassifying Variables By TypeCExamples of Types of VariablesDCOVAQuestionResponsesVariable TypeDo you have a Facebook profile?Yes or NoCategorical (Qualitative)How many text messages have you sent in the past three days?-Numerical(discrete)

4、How long did the mobile app update take to download?-Numerical(continuous)Examples of Types of VariablesTypes of VariablesVariablesCategoricalNumerical DiscreteContinuousExamples:Marital StatusPolitical PartyEye Color (Defined categories)Examples:Number of ChildrenDefects per hour (Counted items)Exa

5、mples:WeightVoltage (Measured characteristics)DCOVATypes of VariablesVariablesDisCollecting Data Correctly Is A Critical TaskNeed to avoid data flawed by biases, ambiguities, or other types of errors.Results from flawed data will be suspect or in error.Even the most sophisticated statistical methods

6、 are not very useful when the data is flawed.DCOVACollecting Data Correctly Is ADeveloping Operational Definitions Is Crucial To Avoid Confusion / ErrorsAn operational definition is a clear and precise statement that provides a common understanding of meaningIn the absence of an operational definiti

7、on miscommunications and errors are likely to occur. Arriving at operational definition(s) is a key part of the Define step of DCOVADCOVADeveloping Operational DefinitEstablishing A Business Objective Focuses Data CollectionExamples Of Business Objectives:A marketing research analyst needs to assess

8、 the effectiveness of a new television advertisement.A pharmaceutical manufacturer needs to determine whether a new drug is more effective than those currently in use.An operations manager wants to monitor a manufacturing process to find out whether the quality of the product being manufactured is c

9、onforming to company standards.An auditor wants to review the financial transactions of a company in order to determine whether the company is in compliance with generally accepted accounting principles.DCOVAEstablishing A Business ObjectSources of DataPrimary Sources: The data collector is the one

10、using the data for analysisData from a political surveyData collected from an experimentObserved dataSecondary Sources: The person performing data analysis is not the data collectorAnalyzing census dataExamining data from print journals or data published on the internet.DCOVASources of DataPrimary S

11、ourcesSources of data fall into five categoriesData distributed by an organization or an individualThe outcomes of a designed experimentThe responses from a surveyThe results of conducting an observational studyData collected by ongoing business activitiesDCOVASources of data fall into fiveExamples

12、Of Data Distributed By Organizations or IndividualsFinancial data on a company provided by investment services.Industry or market data from market research firms and trade associations.Stock prices, weather conditions, and sports statistics in daily newspapers.DCOVAExamples Of Data Distributed BExam

13、ples of Data From A Designed ExperimentConsumer testing of different versions of a product to help determine which product should be pursued further.Material testing to determine which suppliers material should be used in a product.Market testing on alternative product promotions to determine which

14、promotion to use more broadly.DCOVAExamples of Data From A DesignExamples of Survey DataA survey asking people which laundry detergent has the best stain-removing abilitiesPolitical polls of registered voters during political campaigns.People being surveyed to determine their satisfaction with a rec

15、ent product or service experience.DCOVAExamples of Survey DataA surveExamples of Data Collected From Observational StudiesMarket researchers utilizing focus groups to elicit unstructured responses to open-ended questions.Measuring the time it takes for customers to be served in a fast food establish

16、ment.Measuring the volume of traffic through an intersection to determine if some form of advertising at the intersection is justified.DCOVAExamples of Data Collected FroExamples of Data Collected From Ongoing Business ActivitiesA bank studies years of financial transactions to help them identify pa

17、tterns of fraud.Economists utilize data on searches done via Google to help forecast future economic conditions.Marketing companies use tracking data to evaluate the effectiveness of a web site.DCOVAExamples of Data Collected FroData Is Collected From Either A Population or A SamplePOPULATIONA popul

18、ation consists of all the items or individuals about which you want to draw a conclusion. The population is the “l(fā)arge group”SAMPLEA sample is the portion of a population selected for analysis. The sample is the “small group”DCOVAData Is Collected From Either Population vs. SamplePopulationSampleAll

19、 the items or individuals about which you want to draw conclusion(s)A portion of the population of items or individuals DCOVAPopulation vs. SamplePopulatioCollecting Data Via Sampling Is Used When Selecting A Sample IsLess time consuming than selecting every item in the population.Less costly than s

20、electing every item in the population.Less cumbersome and more practical than analyzing the entire population.DCOVACollecting Data Via Sampling IThings To Consider / Deal With In Potential Sources Of DataIs the source of data structured or unstructured?How is electronic data formatted?How is data en

21、coded?DCOVAThings To Consider / Deal WithStructured Data Follows An Organizing Principle & Unstructured Data Does NotA Stock Ticker Provides Structured Data:The stock ticker repeatedly reports a company name, the number of shares last traded, the bid price, and the percent change in the stock price.

22、Due to their inherent structure, data from tables and forms are structured data.E-mails from five people concerning stock trades is an example of unstructured data.In these e-mails you cannot count on the information being shared in a specific order or format.This book deals exclusively with structu

23、red dataDCOVAStructured Data Follows An OrgAll Of The Methods In This Book Deal With Structured DataTo use the techniques in this book on unstructured data you need to convert the unstructured into structured data.For many of the questions you might want to answer, the starting point can / will be t

24、abular data.DCOVAAll Of The Methods In This BooData Can Be Formatted and / or Encoded In More Than One WaySome electronic formats are more readily usable than others.Different encodings can impact the precision of numerical variables and can also impact data compatibility.As you identify and choose

25、sources of data you need to consider / deal with these issuesDCOVAData Can Be Formatted and / oData Cleaning Is Often A Necessary Activity When Collecting DataOften find “irregularities” in the dataTypographical or data entry errorsValues that are impossible or undefinedMissing valuesOutliersWhen fo

26、und these irregularities should be reviewed / addressedBoth Excel & Minitab can be used to address irregularitiesDCOVAData Cleaning Is Often A NecesAfter Collection It Is Often Helpful To Recode Some VariablesRecoding a variable can either supplement or replace the original variable.Recoding a categ

27、orical variable involves redefining categories.Recoding a quantitative variable involves changing this variable into a categorical variable.When recoding be sure that the new categories are mutually exclusive (categories do not overlap) and collectively exhaustive (categories cover all possible valu

28、es).DCOVAAfter Collection It Is Often HA Sampling Process Begins With A Sampling FrameThe sampling frame is a listing of items that make up the populationFrames are data sources such as population lists, directories, or mapsInaccurate or biased results can result if a frame excludes certain portions

29、 of the populationUsing different frames to generate data can lead to dissimilar conclusionsDCOVAA Sampling Process Begins WithTypes of SamplesSamplesNon-Probability SamplesJudgmentProbability SamplesSimple RandomSystematicStratifiedClusterConvenienceDCOVATypes of SamplesSamplesNon-ProTypes of Sampl

30、es:Nonprobability SampleIn a nonprobability sample, items included are chosen without regard to their probability of occurrence.In convenience sampling, items are selected based only on the fact that they are easy, inexpensive, or convenient to sample.In a judgment sample, you get the opinions of pr

31、e-selected experts in the subject matter. DCOVATypes of Samples:NonprobabiliTypes of Samples:Probability SampleIn a probability sample, items in the sample are chosen on the basis of known probabilities.Probability SamplesSimple RandomSystematicStratifiedClusterDCOVATypes of Samples:Probability Prob

32、ability Sample:Simple Random SampleEvery individual or item from the frame has an equal chance of being selectedSelection may be with replacement (selected individual is returned to frame for possible reselection) or without replacement (selected individual isnt returned to the frame).Samples obtain

33、ed from table of random numbers or computer random number generators.DCOVAProbability Sample:Simple RanSelecting a Simple Random Sample Using A Random Number TableSampling Frame For Population With 850 ItemsItem Name Item #Bev R. 001Ulan X. 002. . . . .Joann P. 849Paul F. 850Portion Of A Random Numb

34、er Table49280 88924 35779 00283 81163 0727511100 02340 12860 74697 96644 8943909893 23997 20048 49420 88872 08401The First 5 Items in a simple random sampleItem # 492Item # 808Item # 892 - does not exist so ignoreItem # 435Item # 779Item # 002DCOVASelecting a Simple Random SampDecide on sample size:

35、 nDivide frame of N individuals into groups of k individuals: k=N/nRandomly select one individual from the 1st group Select every kth individual thereafterProbability Sample:Systematic SampleN = 40n = 4k = 10First GroupDCOVADecide on sample size: nProbabProbability Sample:Stratified SampleDivide pop

36、ulation into two or more subgroups (called strata) according to some common characteristicA simple random sample is selected from each subgroup, with sample sizes proportional to strata sizesSamples from subgroups are combined into oneThis is a common technique when sampling population of voters, st

37、ratifying across racial or socio-economic lines.PopulationDividedinto 4strataDCOVAProbability Sample:StratifiedProbability SampleCluster SamplePopulation is divided into several “clusters,” each representative of the populationA simple random sample of clusters is selectedAll items in the selected c

38、lusters can be used, or items can be chosen from a cluster using another probability sampling techniqueA common application of cluster sampling involves election exit polls, where certain election districts are selected and sampled.Population divided into 16 clusters.Randomly selected clusters for sampleDCOVAProbability SampleCluster SamProbability Sample:Comparing Sampling MethodsSimple random sample and Systematic sampleSimple to useMay not be a good representation of the populations underlying characteristicsStratified sampleEnsures representation of individuals across