Downloaded 13 times
Uploaded byCarmeline Coronado
Statistics for Managers Using Microsoft Excel
The document discusses introductory concepts in statistics, including defining statistics, distinguishing between descriptive and inferential statistics, and summarizing sources of data and types of data scales. It covers topics like explaining types of samples, describing the survey process and potential errors, and outlining statistical methods like descriptive statistics and inferential statistics. Various key terms are also defined, such as population, parameter, sample, and statistic.
More Related Content
Similar to Statistics for Managers Using Microsoft Excel
Statistics for Managers Using Microsoft Excel
- 1. 1 -1 -11 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Statistics for Managers UsingStatistics for Managers Using Microsoft ExcelMicrosoft Excel Introduction & Data CollectionIntroduction & Data Collection Chapter 1Chapter 1
- 2. 1 -1 -22 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Learning ObjectivesLearning Objectives Define statisticsDefine statistics Distinguish descriptive & inferentialDistinguish descriptive & inferential statisticsstatistics Summarize the sources of dataSummarize the sources of data Describe the types of data & scalesDescribe the types of data & scales Explain the types of samplesExplain the types of samples Describe survey process & errorsDescribe survey process & errors
- 3. 1 -1 -33 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Introduction to StatisticsIntroduction to Statistics
- 4. 1 -1 -44 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. What Is Statistics?What Is Statistics? Collecting dataCollecting data e.g., Surveye.g., Survey Presenting dataPresenting data e.g., Charts &e.g., Charts & tablestables CharacterizingCharacterizing datadata e.g., Averagee.g., Average
- 5. 1 -1 -55 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. What Is Statistics?What Is Statistics? Collecting dataCollecting data e.g., Surveye.g., Survey Presenting dataPresenting data e.g., Charts &e.g., Charts & tablestables CharacterizingCharacterizing datadata e.g., Averagee.g., Average DataData AnalysisAnalysis Why?Why? © 1984-1994 T/Maker Co.
- 6. 1 -1 -66 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. What Is Statistics?What Is Statistics? Collecting dataCollecting data e.g., Surveye.g., Survey Presenting dataPresenting data e.g., Charts &e.g., Charts & tablestables CharacterizingCharacterizing datadata e.g., Averagee.g., Average DataData AnalysisAnalysis Decision-Decision- MakingMaking © 1984-1994 T/Maker Co. © 1984-1994 T/Maker Co. Why?Why?
- 7. 1 -1 -77 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Application AreasApplication Areas AccountingAccounting AuditingAuditing CostingCosting FinanceFinance Financial trendsFinancial trends ForecastingForecasting ManagementManagement Describe employeesDescribe employees Quality improvementQuality improvement MarketingMarketing Consumer preferencesConsumer preferences Marketing mix effectsMarketing mix effects
- 8. 1 -1 -88 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Statistical MethodsStatistical Methods
- 9. 1 -1 -99 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Statistical MethodsStatistical Methods Statistical Methods Descriptive Statistics Inferential Statistics
- 10. 1 -1 -1010 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Descriptive StatisticsDescriptive Statistics InvolvesInvolves Collecting dataCollecting data Presenting dataPresenting data CharacterizingCharacterizing datadata PurposePurpose Describe dataDescribe data
- 11. 1 -1 -1111 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Descriptive StatisticsDescriptive Statistics InvolvesInvolves Collecting dataCollecting data Presenting dataPresenting data CharacterizingCharacterizing datadata PurposePurpose Describe dataDescribe data X = 30.5 SX = 30.5 S22 = 113= 113 0 25 50 Q1 Q2 Q3 Q4 $
- 12. 1 -1 -1212 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Inferential StatisticsInferential Statistics InvolvesInvolves EstimationEstimation HypothesisHypothesis testingtesting PurposePurpose Make decisionsMake decisions about populationabout population characteristicscharacteristics Population?Population?
- 13. 1 -1 -1313 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Population (universe)Population (universe) All items of interestAll items of interest SampleSample Portion of populationPortion of population ParameterParameter Summary measure about populationSummary measure about population StatisticStatistic Summary measure about sampleSummary measure about sample Key TermsKey Terms
- 14. 1 -1 -1414 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Population (universe)Population (universe) All items of interestAll items of interest SampleSample Portion of populationPortion of population ParameterParameter Summary measure about populationSummary measure about population StatisticStatistic Summary measure about sampleSummary measure about sample Key TermsKey Terms • PP inin PPopulationopulation && PParameterarameter • SS inin SSampleample && SStatistictatistic
- 15. 1 -1 -1515 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Statistical StudiesStatistical Studies
- 16. 1 -1 -1616 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Statistical StudiesStatistical Studies Statistical Studies Enumerative Study Analytical Study
- 17. 1 -1 -1717 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Enumerative StudyEnumerative Study Involves decisionInvolves decision making about amaking about a populationpopulation Frame is listing ofFrame is listing of all population unitsall population units Example:Example: Names inNames in telephone booktelephone book Example: PoliticalExample: Political pollpoll
- 18. 1 -1 -1818 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Analytical StudyAnalytical Study Involves action onInvolves action on a processa process Improves futureImproves future performanceperformance No identifiableNo identifiable universe or frameuniverse or frame e.g., Productione.g., Production processprocess
- 19. 1 -1 -1919 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Analytical StudyAnalytical Study Involves action onInvolves action on a processa process Improves futureImproves future performanceperformance No identifiableNo identifiable universe or frameuniverse or frame e.g., Productione.g., Production processprocess Goods Services Product Focused Process Focused People Equip- ment Material Infor- mation OutputProcessInput Feedback
- 20. 1 -1 -2020 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. DataData
- 21. 1 -1 -2121 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Why Collect Data?Why Collect Data? Obtain input to a research studyObtain input to a research study Measure performanceMeasure performance Assist in formulating decisionAssist in formulating decision alternativesalternatives Satisfy curiositySatisfy curiosity Knowledge for the sake of knowledgeKnowledge for the sake of knowledge
- 22. 1 -1 -2222 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Data TypesData Types Data Numerical (Quantitative) Categorical (Qualitative) Discrete Continuous
- 23. 1 -1 -2323 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Data Type ExamplesData Type Examples NumericalNumerical DiscreteDiscrete To how many magazines do you subscribeTo how many magazines do you subscribe currently? ___ (Number)currently? ___ (Number) ContinuousContinuous How tall are you? ___ (Inches)How tall are you? ___ (Inches) CategoricalCategorical Do you own savings bonds? __ Yes __ NoDo you own savings bonds? __ Yes __ No
- 24. 1 -1 -2424 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. How Are Data Measured?How Are Data Measured? Nominal scaleNominal scale CategoriesCategories e.g., Male-femalee.g., Male-female CountCount Ordinal scaleOrdinal scale CategoriesCategories Ordering impliedOrdering implied e.g., High-lowe.g., High-low CountCount
- 25. 1 -1 -2525 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Interval scaleInterval scale Equal intervalsEqual intervals No true 0No true 0 e.g., Degrees Celsiuse.g., Degrees Celsius MeasurementMeasurement Ratio scaleRatio scale Equal intervalsEqual intervals True 0True 0 Meaningful ratiosMeaningful ratios e.g., Height in inchese.g., Height in inches How Are Data Measured?How Are Data Measured? Nominal scaleNominal scale CategoriesCategories e.g., Male-femalee.g., Male-female CountCount Ordinal scaleOrdinal scale CategoriesCategories Ordering impliedOrdering implied e.g., High-lowe.g., High-low CountCount
- 26. 1 -1 -2626 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Are they categorical? NumericalAre they categorical? Numerical (discrete or continuous)? What scale?(discrete or continuous)? What scale? Thinking ChallengeThinking Challenge GenderGender Male, femaleMale, female WeightWeight 123, 140.2 etc.123, 140.2 etc. Auto speedAuto speed 78, 64, 45 etc.78, 64, 45 etc. TemperatureTemperature 78, 64, 85 etc.78, 64, 85 etc. # Siblings# Siblings 0-2, 3-5, 6+0-2, 3-5, 6+ Letter gradeLetter grade A, B, C etc.A, B, C etc. AloneAlone GroupGroup ClassClass
- 27. 1 -1 -2727 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Data SourcesData Sources Data Sources Primary Secondary Experiment Published (& On-Line) Survey Observation
- 28. 1 -1 -2828 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. SurveysSurveys
- 29. 1 -1 -2929 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Survey StepsSurvey Steps Define purposeDefine purpose DesignDesign questionnairequestionnaire Select sampleSelect sample designdesign Sample typeSample type Sample sizeSample size
- 30. 1 -1 -3030 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Collect dataCollect data (field work)(field work) Prepare dataPrepare data EditEdit CodeCode Analyze dataAnalyze data Interpret findingsInterpret findings Report resultsReport results Define purposeDefine purpose DesignDesign questionnairequestionnaire Select sampleSelect sample designdesign Sample typeSample type Sample sizeSample size Survey StepsSurvey Steps
- 31. 1 -1 -3131 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Questionnaire DesignQuestionnaire Design Question contentQuestion content Mode of responseMode of response Question wordingQuestion wording Question sequenceQuestion sequence LayoutLayout Pilot testingPilot testing © 1984-1994 T/Maker Co.
- 32. 1 -1 -3232 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. SamplesSamples
- 33. 1 -1 -3333 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Why Sample?Why Sample? Destruction of testDestruction of test unitsunits Quality controlQuality control Accurate &Accurate & reliable resultsreliable results Pragmatic reasonsPragmatic reasons TimeTime CostCost
- 34. 1 -1 -3434 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Types of SamplesTypes of Samples Typeof Sample Probability Non Probability Simple Random StratifiedSystematic Cluster Chunk Judg- ment Quota
- 35. 1 -1 -3535 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Simple Random SampleSimple Random Sample
- 36. 1 -1 -3636 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Types of SamplesTypes of Samples Typeof Sample Probability Non Probability Simple Random StratifiedSystematic Cluster Chunk Judg- ment Quota
- 37. 1 -1 -3737 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. SimpleSimple Random SampleRandom Sample Each population elementEach population element has anhas an equal chanceequal chance ofof being selectedbeing selected Selecting 1 subject doesSelecting 1 subject does not affect selectingnot affect selecting othersothers May use random numberMay use random number table, lottery, ‘fish bowl’table, lottery, ‘fish bowl’ © 1984-1994 T/Maker Co.
- 38. 1 -1 -3838 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Random Number TableRandom Number Table Column 00000 00001 11111 11111 Row 12345 67890 12345 67890 01 49280 88924 35779 00283 02 61870 41657 07468 08612 03 43898 65923 25078 86129
- 39. 1 -1 -3939 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Systematic SampleSystematic Sample
- 40. 1 -1 -4040 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Types of SamplesTypes of Samples Typeof Sample Probability Non Probability Simple Random StratifiedSystematic Cluster Chunk Judg- ment Quota
- 41. 1 -1 -4141 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Systematic SampleSystematic Sample EveryEvery kkth element Isth element Is selected after aselected after a random start withinrandom start within the firstthe first kk elementselements Skip interval,Skip interval, kk, is, is Population sizePopulation size Sample sizeSample size Used in telephoneUsed in telephone surveyssurveys © 1984-1994 T/Maker Co.
- 42. 1 -1 -4242 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Stratified SampleStratified Sample
- 43. 1 -1 -4343 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Types of SamplesTypes of Samples Typeof Sample Probability Non Probability Simple Random StratifiedSystematic Cluster Chunk Judg- ment Quota
- 44. 1 -1 -4444 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Stratified SampleStratified Sample Divide population intoDivide population into subgroupssubgroups Mutually exclusiveMutually exclusive ExhaustiveExhaustive At least 1 commonAt least 1 common characteristic ofcharacteristic of interestinterest Select simple randomSelect simple random samples fromsamples from subgroupssubgroups All StudentsAll Students CommuterCommuter ss ResidentsResidents SampleSample
- 45. 1 -1 -4545 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Cluster SampleCluster Sample
- 46. 1 -1 -4646 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Types of SamplesTypes of Samples Typeof Sample Probability Non Probability Simple Random StratifiedSystematic Cluster Chunk Judg- ment Quota
- 47. 1 -1 -4747 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Cluster SampleCluster Sample Divide populationDivide population into clustersinto clusters If managersIf managers are elements thenare elements then companies are clusterscompanies are clusters Select clustersSelect clusters randomlyrandomly Survey all or a randomSurvey all or a random sample of elements insample of elements in clustercluster Companies (Clusters)Companies (Clusters) SampleSample
- 48. 1 -1 -4848 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Nonprobability SamplesNonprobability Samples
- 49. 1 -1 -4949 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Types of SamplesTypes of Samples Typeof Sample Probability Non Probability Simple Random StratifiedSystematic Cluster Chunk Judg- ment Quota
- 50. 1 -1 -5050 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Nonprobability SamplesNonprobability Samples JudgmentJudgment Use experience to select sampleUse experience to select sample Example: Test marketsExample: Test markets QuotaQuota Similar to stratified samplingSimilar to stratified sampling except no random samplingexcept no random sampling Chunk (convenience)Chunk (convenience) Use elements most availableUse elements most available
- 51. 1 -1 -5151 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. Errors Due to SamplingErrors Due to Sampling TotalPopulation (Students) SampleFrame (Students in PhoneBook) PlannedSample (SelectedStudents) Actual Sample Coverage(Frame)Error SamplingError Nonresponse& MeasurementError
- 52. 1 -1 -5252 © 1997 Prentice-Hall, Inc.© 1997 Prentice-Hall, Inc. ConclusionConclusion Defined statisticsDefined statistics Distinguished descriptive &Distinguished descriptive & inferential statisticsinferential statistics Summarized the sources of dataSummarized the sources of data Described the types of data & scalesDescribed the types of data & scales Explained the types of samplesExplained the types of samples Described survey process & errorsDescribed survey process & errors Source: www.uic.edu/classes/idsc/ids531/lbsppt/chap01.ppt
Editor's Notes
- #5 :1, 1, 3
- #6 :1, 1, 3
- #7 :1, 1, 3
- #14 Data facts or information that is relevant or appropriate to a decision maker Population the totality of objects under consideration Sample a portion of the population that is selected for analysis Parameter a summary measure (e.g., mean) that is computed to describe a characteristic of the population Statistic a summary measure (e.g., mean) that is computed to describe a characteristic of the sample
- #15 Data facts or information that is relevant or appropriate to a decision maker Population the totality of objects under consideration Sample a portion of the population that is selected for analysis Parameter a summary measure (e.g., mean) that is computed to describe a characteristic of the population Statistic a summary measure (e.g., mean) that is computed to describe a characteristic of the sample
- #27 1. Gender = categorical - nominal 2. Weight = numerical - continuous - ratio 3. Auto Speed = numerical - continuous - ratio 4. Temperature = numerical - continuous - interval 5. # Siblings = categorical - ordinal 6. Letter Grade = categorical - ordinal
- #32 Question Content Related to research purpose Based on respondent’s ability to answer accurately Response Format open-ended Vs. fixed alternative (closed-ended) Question Wording simple, clear words avoid leading questions: ‘In view of the health crisis, it would be best to nationalize the health industry.’ Question Sequence use simple & interesting opening questions general questions first Layout More important for Mail Survey than telephone survey Pretest Shows where you have asked ambiguous questions
- #34 Pragmatic Reasons If Chrysler wished to census past purchasers’ reactions, millions of car buyers would have to be contacted Accurate & Reliable Results Reasonable accuracy though not perfect - sampling error! May be more accurate than census since less chance of nonsampling errors (e.g., data entry) Bureau of the Census uses samples to check the accuracy of the US Census. If the sample shows possible source of error, the census is redone. Destruction of Test Units e.g., Mean Life of Light Bulbs
- #35 Probability Samples Selection is based on chance Subjects are chosen based on some known probabilities Eliminates or reduces bias Random refers to procedure not the data: The outcome cannot be predicted because it is dependent upon chance Non Probability Samples Do not have above characteristics Done for time and convenience
- #37 Probability Samples Selection is based on chance Subjects are chosen based on some known probabilities Eliminates or reduces bias Random refers to procedure not the data: The outcome cannot be predicted because it is dependent upon chance Non Probability Samples Do not have above characteristics Done for time and convenience
- #38 Simple Random Use random number table Number of digits is determined by population size
- #39 Columns are 01, 02 etc. (aligned vertically) Example Population size is 50. Sample size is 10. Since population size (50) has 2-digits, divide table into 2 digit numbers. Begin top left (for convenience only). 1-49, 2-28, 3-08, 4-89 (skip) 4-24, 5-35, 6-77 (skip), 6-90 (skip) 6-02, 7-83 (skip) 7-61(skip) 7-87 (skip) 7-04, 8-16, 9-57 (skip) 9-07, 10-46. Example Population size is 100. Use 3 digit numbers.
- #41 Probability Samples Selection is based on chance Subjects are chosen based on some known probabilities Eliminates or reduces bias Random refers to procedure not the data: The outcome cannot be predicted because it is dependent upon chance Non Probability Samples Do not have above characteristics Done for time and convenience
- #42 Systematic Requires all population elements Bias may occur due to periodicity In the telephone book example, unlisted numbers will not be found Example: Sampling frame is 100 individuals. You want to select 20. Select first name by random number, then every 5th person.
- #44 Probability Samples Selection is based on chance Subjects are chosen based on some known probabilities Eliminates or reduces bias Random refers to procedure not the data: The outcome cannot be predicted because it is dependent upon chance Non Probability Samples Do not have above characteristics Done for time and convenience
- #45 Stratified Assures 1. Sample reflects population in terms of criterion used for stratifying. 2. More efficient sample - sampling error is reduced. Example: College has 70% on-campus students and 30% commuters. A 100 student survey would get close to 70 on-campus students and 30 commuters. A simple random survey might get 60 on-campus and 40 commuting students. Similar to Quota sampling except that a simple random sample is drawn from each strata.
- #47 Probability Samples Selection is based on chance Subjects are chosen based on some known probabilities Eliminates or reduces bias Random refers to procedure not the data: The outcome cannot be predicted because it is dependent upon chance Non Probability Samples Do not have above characteristics Done for time and convenience
- #48 Cluster Idea is to sample economically yet retain characteristics of probability sample. Ideally, cluster is as heterogeneous as the population. Often, characteristics of elements in cluster may be similar.
- #50 Probability Samples Selection is based on chance Subjects are chosen based on some known probabilities Eliminates or reduces bias Random refers to procedure not the data: The outcome cannot be predicted because it is dependent upon chance Non Probability Samples Do not have above characteristics Done for time and convenience
- #51 Judgment A fashion manufacturer selects key accounts to predict what will sell next season Quota Advantages are speed of data collection, lower costs, and convenience. Often used in laboratory experiments It is difficult to find a sample of the general population willing to visit a laboratory Chunk (Convenience) Street interviews at election time. Views represent supposedly the entire population. Need impressions of text book in an hour. Use this class to represent all students.
- #52 Frame Error The sampling frame is also called the ‘working population.’ Frame error is the discrepancy between population and sampling frame. e.g., Not all students may be in phone book Sampling Error Sampling units may not perfectly represent the population. All samples vary. Sampling error is a function of sample size Systematic (Nonresponse & Measurement) Error Nonresponse, badly worded questions, interview error.