McGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.WEEK 5 DATA ANALYSIS APPROACHESO...
15-2Goal of Data Decription“The goal is to transform data intoinformation, and information into insight.Carly Fiorinaforme...
15-3PulsePoint:Research Revelation55 The percent of white-collar workerswho answer work-related calls or e-mail after work...
15-4Data Preparationin the Research Process
15-5MonitoringOnline Survey DataOnline surveys needspecial editing attention.CfMC provides softwareand support to research...
15-6EditingCriteriaConsistentUniformlyenteredArranged forsimplificationCompleteAccurate
15-7Field EditingSpeed without accuracy won’thelp the manager choose theright direction.•Field editing review•Entry gaps i...
15-8Central EditingBe familiar with instructionsgiven to interviewers and codersDo not destroy the original entryMake all ...
15-9Sample Codebook
15-10Precoding
15-11CodingOpen-Ended Questions6. What prompted you to purchase yourmost recent life insurance policy?____________________...
15-12Coding RulesCategoriesshould beAppropriate to theresearch problemExhaustiveMutually exclusiveDerived from oneclassifi...
15-13Content AnalysisQSR’s XSightsoftware forcontent analysis.
15-14Content Analysis
15-15Types of Content AnalysisSyntacticalPropositionalReferentialThematic
15-16Open-Question CodingLocus ofResponsibility MentionedNotMentionedA. Company___________________________________________...
15-17Handling “Don’t Know”ResponsesQuestion: Do you have a productive relationshipwith your present salesperson?Years ofPu...
15-18Data EntryDatabaseProgramsOpticalRecognitionDigital/BarcodesVoicerecognitionKeyboarding
15-19Missing DataListwise DeletionPairwise DeletionReplacement
15-20Key Terms• Bar code• Codebook• Coding• Content analysis• Data entry• Data field• Data file• Data preparation• Data re...
Appendix 15aDescribing DataStatisticallyMcGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights R...
15-22Research Adjusts for ImperfectData“In the future, we’ll stop moaning about thelack of perfect data and start using th...
15-23FrequenciesUnit SalesIncrease(%) Frequency PercentageCumulativePercentage56789Total12321911.122.233.322.211.1100.011....
15-24Distributions
15-25Characteristics of Distributions
15-26Measures of Central TendencyMean ModeMedian
15-27Measures of VariabilityInterquartilerangeQuartiledeviationRangeStandarddeviationVariance
15-28Summarizing Distribution Shape
15-29Variable Population SampleMean µ XProportion  pVariance 2s2Standard deviation  sSize N nStandard error of the mean...
15-30Key Terms• Central tendency• Descriptive statistics• Deviation scores• Frequency distribution• Interquartile range (I...
15-31
Chapter 16Exploring, Displaying,and Examining DataMcGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. Al...
16-33Learning ObjectivesUnderstand . . .• That exploratory data analysis techniquesprovide insights and data diagnostics b...
16-34Research asCompetitive Advantage“As data availability continues to increase, theimportance of identifying/filtering a...
16-35PulsePoint:Research Revelation65 The percent boost in companyrevenue created by best practices indata quality.
16-36Researcher Skill Improves DataDiscoveryDDW is a global player inresearch services. As thisad proclaims, you can“push ...
16-37Exploratory Data AnalysisConfirmatoryExploratory
16-38Data Exploration, Examination,and Analysis in the ResearchProcess
16-39Research Values theUnexpected“It is precisely because the unexpected jolts usout of our preconceived notions, ourassu...
16-40Frequency of Ad RecallValue Label Value Frequency Percent Valid CumulativePercent Percent
16-41Bar Chart
16-42Pie Chart
16-43Frequency Table
16-44Histogram
16-45Stem-and-Leaf Display45566678888912466799022356780226824018310633636856789101112131415161718192021
16-46Pareto Diagram
16-47Boxplot Components
16-48Diagnostics with Boxplots
16-49Boxplot Comparison
16-50Mapping
16-51Geograph: Digital Camera Ownership
16-52SPSS Cross-Tabulation
16-53Percentages inCross-Tabulation
16-54Guidelines for Using PercentagesAveraging percentagesUse of too large percentagesUsing too small a basePercentage dec...
16-55Cross-Tabulation with Controland Nested Variables
16-56Automatic Interaction Detection(AID)
16-57Exploratory Data AnalysisThis Booth ResearchServices ad suggests thatthe researcher’s role is tomake sense of datadis...
16-58Key Terms• Automatic interactiondetection (AID)• Boxplot• Cell• Confirmatory dataanalysis• Contingency table• Control...
Working withData TablesMcGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
16-60Original Data TableOur grateful appreciation to eMarketer for
16-61Arranged by Spending
16-62Arranged byNo. of Purchases
16-63Arranged by Avg. Transaction,Highest
16-64Arranged by Avg. Transaction,Lowest
15-65
Chapter 17Hypothesis TestingMcGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
17-67Learning ObjectivesUnderstand . . .• The nature and logic of hypothesis testing.• A statistically significant differe...
17-68Learning ObjectivesUnderstand . . .• The differences between parametric andnonparametric tests and when to use each.•...
17-69Hypothesis Testingvs. Theory“Don’t confuse “hypothesis” and “theory.”The former is a possible explanation; thelatter,...
17-70PulsePoint:Research Revelation$28 The amount, in billions, saved byNorth American companies byhaving employees use a ...
17-71Hypothesis TestingDeductiveReasoningInductiveReasoning
17-72Hypothesis Testing Finds Truth“One finds the truth by making ahypothesis and comparing the truth tothe hypothesis.”Da...
17-73Statistical ProceduresDescriptiveStatisticsInferentialStatistics
17-74Hypothesis Testingand the Research Process
17-75When Data Present a ClearPictureAs Abacus states inthis ad, whenresearchers ‘siftthrough the chaos’ and‘find what mat...
17-76Approaches to HypothesisTestingClassical statistics• Objective view ofprobability• Establishedhypothesis is rejectedo...
17-77Statistical Significance
17-78Types of HypothesesNull– H0:  = 50 mpg– H0:  < 50 mpg– H0:  > 50 mpgAlternate– HA:  = 50 mpg– HA:  > 50 mpg– HA:...
17-79Two-Tailed Test of Significance
17-80One-Tailed Test of Significance
17-81Decision RuleTake no corrective action if theanalysis shows that one cannotreject the null hypothesis.
17-82Statistical Decisions
17-83Probability of Making a Type IError
17-84Critical Values
17-85Exhibit 17-4 Probability ofMaking A Type I Error
17-86Factors Affecting Probability ofCommitting a  ErrorTrue value of parameterAlpha level selectedOne or two-tailed test...
17-87Probability of Making A Type IIError
17-88Statistical TestingProceduresObtain criticaltest valueInterpret thetestStagesChoosestatistical testState nullhypothes...
17-89Tests of SignificanceNonparametricParametric
17-90Assumptions for UsingParametric TestsIndependent observationsNormal distributionEqual variancesInterval or ratio scales
17-91Probability Plot
17-92Probability Plot
17-93Probability Plot
17-94Advantages of NonparametricTestsEasy to understand and useUsable with nominal dataAppropriate for ordinal dataAppropr...
17-95How to Select a TestHow many samples are involved?If two or more samples:are the individual cases independent or rela...
17-96Recommended StatisticalTechniquesTwo-Sample Tests____________________________________________k-Sample Tests__________...
17-97Questions Answered byOne-Sample Tests• Is there a difference between observedfrequencies and the frequencies we would...
17-98Parametric Testst-testZ-test
17-99One-Sample t-Test ExampleNull Ho: = 50 mpgStatistical test t-testSignificance level .05, n=100Calculated value 1.786C...
17-100One Sample Chi-Square TestExampleLiving ArrangementIntendto JoinNumberInterviewedPercent(no. interviewed/200)Expecte...
17-101One-Sample Chi-SquareExampleNull Ho: 0 = EStatistical test One-sample chi-squareSignificance level .05Calculated val...
17-102Two-Sample Parametric Tests
17-103Two-Sample t-Test ExampleA Group B GroupAveragehourly salesX1 =$1,500X2 =$1,300Standarddeviations1 = 225 s2 = 251
17-104Two-Sample t-Test ExampleNull Ho: A sales = B salesStatistical test t-testSignificance level .05 (one-tailed)Calcula...
17-105Two-Sample NonparametricTests: Chi-SquareOn-the-Job-AccidentCell DesignationCountExpected Values Yes No Row TotalSmo...
17-106Two-Sample Chi-SquareExampleNull There is no difference indistribution channel for agecategories.Statistical test Ch...
17-107SPSS Cross-TabulationProcedure
17-108Two-Related-Samples TestsNonparametricParametric
17-109Sales Data forPaired-Samples t-TestCompanySalesYear2SalesYear 1 Difference D D2GMGEExxonIBMFordAT&TMobilDuPontSearsA...
17-110Paired-Samples t-Test ExampleNull Year 1 sales = Year 2 salesStatistical test Paired sample t-testSignificance level...
17-111SPSS Output for Paired-Samples t-Test
17-112Related Samples NonparametricTests: McNemar TestBeforeAfterDo Not FavorAfterFavorFavor A BDo Not Favor C D
17-113Related Samples NonparametricTests: McNemar TestBeforeAfterDo Not FavorAfterFavorFavor A=10 B=90Do Not Favor C=60 D=40
17-114k-Independent-Samples Tests:ANOVATests the null hypothesis that the means of threeor more populations are equalOne-w...
17-115ANOVA Example__________________________________________ModelSummary_________________________________________Source d...
17-116ANOVA Example ContinuedNull A1 = A2 = A3Statistical test ANOVA and F ratioSignificance level .05Calculated value ...
17-117Post Hoc: Scheffe’s S MultipleComparison ProcedureVerses DiffCrit.Diff. p ValueLufthansa MalaysiaAirlines19,950 11.4...
17-118Multiple Comparison ProceduresTestComplexComparisonsPairwiseComparisonsEqualn’sOnlyUnequaln’sEqualVariancesAssumedUn...
17-119ANOVA PlotsLufthansaBusinessClassLounge
17-120Two-Way ANOVA Example_______________________________________Model Summary___________________________Source d.f.Sum o...
17-121k-Related-Samples TestsMore than two levels ingrouping factorObservations are matchedData are interval or ratio
17-122Repeated-Measures ANOVAExampleAll data are hypothetical.___________________________________Means Table by Airline __...
17-123Key Terms• a priori contrasts• Alternative hypothesis• Analysis of variance(ANOVA• Bayesian statistics• Chi-square t...
17-124Key Terms• Null hypothesis• Observed significancelevel• One-sample tests• One-tailed test• p value• Parametric tests...
17-125Key Terms• Two-related-samplestests• Two-tailed test• Type I error• Type II error• Z distribution• Z test
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5 data analysis approaches dr. hueihsia holloman

  1. 1. McGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.WEEK 5 DATA ANALYSIS APPROACHESObjectives1. Prepare collected data for analysis.2. Differentiate between descriptive statistics &inferential statistics.3. Determine the overall data analysisapproach for a data set.
  2. 2. 15-2Goal of Data Decription“The goal is to transform data intoinformation, and information into insight.Carly Fiorinaformer president and chairwoman,Hewlett-Packard Co
  3. 3. 15-3PulsePoint:Research Revelation55 The percent of white-collar workerswho answer work-related calls or e-mail after work hours.
  4. 4. 15-4Data Preparationin the Research Process
  5. 5. 15-5MonitoringOnline Survey DataOnline surveys needspecial editing attention.CfMC provides softwareand support to researchsuppliers to preventinterruptions fromdamaging data .
  6. 6. 15-6EditingCriteriaConsistentUniformlyenteredArranged forsimplificationCompleteAccurate
  7. 7. 15-7Field EditingSpeed without accuracy won’thelp the manager choose theright direction.•Field editing review•Entry gaps identified•Callbacks made•Validate results
  8. 8. 15-8Central EditingBe familiar with instructionsgiven to interviewers and codersDo not destroy the original entryMake all editing entries identifiable and instandardized formInitial all answers changed or suppliedPlace initials and date of editingon each instrument completed
  9. 9. 15-9Sample Codebook
  10. 10. 15-10Precoding
  11. 11. 15-11CodingOpen-Ended Questions6. What prompted you to purchase yourmost recent life insurance policy?________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
  12. 12. 15-12Coding RulesCategoriesshould beAppropriate to theresearch problemExhaustiveMutually exclusiveDerived from oneclassification principle
  13. 13. 15-13Content AnalysisQSR’s XSightsoftware forcontent analysis.
  14. 14. 15-14Content Analysis
  15. 15. 15-15Types of Content AnalysisSyntacticalPropositionalReferentialThematic
  16. 16. 15-16Open-Question CodingLocus ofResponsibility MentionedNotMentionedA. Company________________________________________________B. Customer________________________________________________C. Joint Company-Customer________________________________________________F. Other________________________________________________Locus ofResponsibilityFrequency (n =100)A. Management1. Sales manager2. Sales process3. Other4. No action areaidentifiedB. Management1. TrainingC. Customer1. Buying processes2. Other3. No action areaidentifiedD. EnvironmentalconditionsE. TechnologyF. Other10207315128520
  17. 17. 15-17Handling “Don’t Know”ResponsesQuestion: Do you have a productive relationshipwith your present salesperson?Years ofPurchasing Yes No Don’t KnowLess than 1 year 10% 40% 38%1 – 3 years 30 30 324 years or more 60 30 30Total100%n = 650100%n = 150100%n = 200
  18. 18. 15-18Data EntryDatabaseProgramsOpticalRecognitionDigital/BarcodesVoicerecognitionKeyboarding
  19. 19. 15-19Missing DataListwise DeletionPairwise DeletionReplacement
  20. 20. 15-20Key Terms• Bar code• Codebook• Coding• Content analysis• Data entry• Data field• Data file• Data preparation• Data record• Database• Don’t know response• Editing• Missing data• Optical characterrecognition• Optical markrecognition• Precoding• Spreadsheet• Voice recognition
  21. 21. Appendix 15aDescribing DataStatisticallyMcGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
  22. 22. 15-22Research Adjusts for ImperfectData“In the future, we’ll stop moaning about thelack of perfect data and start using the gooddata with much more advanced analytics anddata-matching techniques.”Kate Lynchresearch directorLeo Burnett’s Starcom Media Unit
  23. 23. 15-23FrequenciesUnit SalesIncrease(%) Frequency PercentageCumulativePercentage56789Total12321911.122.233.322.211.1100.011.133.366.788.9100Unit SalesIncrease(%) Frequency PercentageCumulativePercentageOrigin, foreign(1)67812211.122.222.211.133.355.5Origin, foreign(2)5679Total1111911.111.111.111.1100.066.677.788.8100.0AB
  24. 24. 15-24Distributions
  25. 25. 15-25Characteristics of Distributions
  26. 26. 15-26Measures of Central TendencyMean ModeMedian
  27. 27. 15-27Measures of VariabilityInterquartilerangeQuartiledeviationRangeStandarddeviationVariance
  28. 28. 15-28Summarizing Distribution Shape
  29. 29. 15-29Variable Population SampleMean µ XProportion  pVariance 2s2Standard deviation  sSize N nStandard error of the mean x SxStandard error of the proportion p Sp___Symbols
  30. 30. 15-30Key Terms• Central tendency• Descriptive statistics• Deviation scores• Frequency distribution• Interquartile range (IQR)• Kurtosis• Median• Mode• Normal distribution• Quartile deviation (Q)• Skewness• Standard deviation• Standard normaldistribution• Standard score (Z score)• Variability• Variance
  31. 31. 15-31
  32. 32. Chapter 16Exploring, Displaying,and Examining DataMcGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
  33. 33. 16-33Learning ObjectivesUnderstand . . .• That exploratory data analysis techniquesprovide insights and data diagnostics byemphasizing visual representations of the data.• How cross-tabulation is used to examinerelationships involving categorical variables,serves as a framework for later statisticaltesting, and makes an efficient tool for datavisualization and later decision-making.
  34. 34. 16-34Research asCompetitive Advantage“As data availability continues to increase, theimportance of identifying/filtering and analyzingrelevant data can be a powerful way to gain aninformation advantage over our competition.”Tom H.C. Andersonfounder & managing partnerAnderson Analytics, LLC
  35. 35. 16-35PulsePoint:Research Revelation65 The percent boost in companyrevenue created by best practices indata quality.
  36. 36. 16-36Researcher Skill Improves DataDiscoveryDDW is a global player inresearch services. As thisad proclaims, you can“push data into a templateand get the job done,” butyou are unlikely to makediscoveries using atemplate process.
  37. 37. 16-37Exploratory Data AnalysisConfirmatoryExploratory
  38. 38. 16-38Data Exploration, Examination,and Analysis in the ResearchProcess
  39. 39. 16-39Research Values theUnexpected“It is precisely because the unexpected jolts usout of our preconceived notions, ourassumptions, our certainties, that it is such afertile source of innovation.”Peter Drucker, authorInnovation and Entrepreneurship
  40. 40. 16-40Frequency of Ad RecallValue Label Value Frequency Percent Valid CumulativePercent Percent
  41. 41. 16-41Bar Chart
  42. 42. 16-42Pie Chart
  43. 43. 16-43Frequency Table
  44. 44. 16-44Histogram
  45. 45. 16-45Stem-and-Leaf Display45566678888912466799022356780226824018310633636856789101112131415161718192021
  46. 46. 16-46Pareto Diagram
  47. 47. 16-47Boxplot Components
  48. 48. 16-48Diagnostics with Boxplots
  49. 49. 16-49Boxplot Comparison
  50. 50. 16-50Mapping
  51. 51. 16-51Geograph: Digital Camera Ownership
  52. 52. 16-52SPSS Cross-Tabulation
  53. 53. 16-53Percentages inCross-Tabulation
  54. 54. 16-54Guidelines for Using PercentagesAveraging percentagesUse of too large percentagesUsing too small a basePercentage decreases cannever exceed 100%
  55. 55. 16-55Cross-Tabulation with Controland Nested Variables
  56. 56. 16-56Automatic Interaction Detection(AID)
  57. 57. 16-57Exploratory Data AnalysisThis Booth ResearchServices ad suggests thatthe researcher’s role is tomake sense of datadisplays.Great data exploration andanalysis delivers insightfrom data.
  58. 58. 16-58Key Terms• Automatic interactiondetection (AID)• Boxplot• Cell• Confirmatory dataanalysis• Contingency table• Control variable• Cross-tabulation• Exploratory dataanalysis (EDA)• Five-number summary• Frequency table• Histogram• Interquartile range (IQR)• Marginals• Nonresistant statistics• Outliers• Pareto diagram• Resistant statistics• Stem-and-leaf display
  59. 59. Working withData TablesMcGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
  60. 60. 16-60Original Data TableOur grateful appreciation to eMarketer for
  61. 61. 16-61Arranged by Spending
  62. 62. 16-62Arranged byNo. of Purchases
  63. 63. 16-63Arranged by Avg. Transaction,Highest
  64. 64. 16-64Arranged by Avg. Transaction,Lowest
  65. 65. 15-65
  66. 66. Chapter 17Hypothesis TestingMcGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
  67. 67. 17-67Learning ObjectivesUnderstand . . .• The nature and logic of hypothesis testing.• A statistically significant difference• The six-step hypothesis testing procedure.
  68. 68. 17-68Learning ObjectivesUnderstand . . .• The differences between parametric andnonparametric tests and when to use each.• The factors that influence the selection of anappropriate test of statistical significance.• How to interpret the various test statistics
  69. 69. 17-69Hypothesis Testingvs. Theory“Don’t confuse “hypothesis” and “theory.”The former is a possible explanation; thelatter, the correct one. The establishmentof theory is the very purpose of science.”Martin H. Fischerprofessor emeritus. physiologyUniversity of Cincinnati
  70. 70. 17-70PulsePoint:Research Revelation$28 The amount, in billions, saved byNorth American companies byhaving employees use a companypurchasing card.
  71. 71. 17-71Hypothesis TestingDeductiveReasoningInductiveReasoning
  72. 72. 17-72Hypothesis Testing Finds Truth“One finds the truth by making ahypothesis and comparing the truth tothe hypothesis.”David DouglassphysicistUniversity of Rochester
  73. 73. 17-73Statistical ProceduresDescriptiveStatisticsInferentialStatistics
  74. 74. 17-74Hypothesis Testingand the Research Process
  75. 75. 17-75When Data Present a ClearPictureAs Abacus states inthis ad, whenresearchers ‘siftthrough the chaos’ and‘find what matters’ theyexperience the “ah ha!”moment.
  76. 76. 17-76Approaches to HypothesisTestingClassical statistics• Objective view ofprobability• Establishedhypothesis is rejectedor fails to be rejected• Analysis based onsample dataBayesian statistics• Extension of classicalapproach• Analysis based onsample data• Also considersestablishedsubjective probabilityestimates
  77. 77. 17-77Statistical Significance
  78. 78. 17-78Types of HypothesesNull– H0:  = 50 mpg– H0:  < 50 mpg– H0:  > 50 mpgAlternate– HA:  = 50 mpg– HA:  > 50 mpg– HA:  < 50 mpg
  79. 79. 17-79Two-Tailed Test of Significance
  80. 80. 17-80One-Tailed Test of Significance
  81. 81. 17-81Decision RuleTake no corrective action if theanalysis shows that one cannotreject the null hypothesis.
  82. 82. 17-82Statistical Decisions
  83. 83. 17-83Probability of Making a Type IError
  84. 84. 17-84Critical Values
  85. 85. 17-85Exhibit 17-4 Probability ofMaking A Type I Error
  86. 86. 17-86Factors Affecting Probability ofCommitting a  ErrorTrue value of parameterAlpha level selectedOne or two-tailed test usedSample standard deviationSample size
  87. 87. 17-87Probability of Making A Type IIError
  88. 88. 17-88Statistical TestingProceduresObtain criticaltest valueInterpret thetestStagesChoosestatistical testState nullhypothesisSelect level ofsignificanceComputedifferencevalue
  89. 89. 17-89Tests of SignificanceNonparametricParametric
  90. 90. 17-90Assumptions for UsingParametric TestsIndependent observationsNormal distributionEqual variancesInterval or ratio scales
  91. 91. 17-91Probability Plot
  92. 92. 17-92Probability Plot
  93. 93. 17-93Probability Plot
  94. 94. 17-94Advantages of NonparametricTestsEasy to understand and useUsable with nominal dataAppropriate for ordinal dataAppropriate for non-normalpopulation distributions
  95. 95. 17-95How to Select a TestHow many samples are involved?If two or more samples:are the individual cases independent or related?Is the measurement scalenominal, ordinal, interval, or ratio?
  96. 96. 17-96Recommended StatisticalTechniquesTwo-Sample Tests____________________________________________k-Sample Tests____________________________________________Measurement ScaleOne-SampleCaseRelatedSamplesIndependentSamplesRelatedSamplesIndependentSamplesNominal • Binomial• x2 one-sampletest• McNemar • Fisher exacttest• x2 two-samples test• Cochran Q • x2 for ksamplesOrdinal • Kolmogorov-Smirnov one-sample test• Runs test• Sign test• Wilcoxonmatched-pairs test• Median test• Mann-Whitney U• Kolmogorov-Smirnov• Wald-Wolfowitz• Friedmantwo-wayANOVA• Medianextension• Kruskal-Wallis one-way ANOVAInterval andRatio• t-test• Z test• t-test forpairedsamples• t-test• Z test• Repeated-measuresANOVA• One-wayANOVA• n-wayANOVA
  97. 97. 17-97Questions Answered byOne-Sample Tests• Is there a difference between observedfrequencies and the frequencies we wouldexpect?• Is there a difference between observed andexpected proportions?• Is there a significant difference between somemeasures of central tendency and thepopulation parameter?
  98. 98. 17-98Parametric Testst-testZ-test
  99. 99. 17-99One-Sample t-Test ExampleNull Ho: = 50 mpgStatistical test t-testSignificance level .05, n=100Calculated value 1.786Critical test value 1.66(from Appendix C,Exhibit C-2)
  100. 100. 17-100One Sample Chi-Square TestExampleLiving ArrangementIntendto JoinNumberInterviewedPercent(no. interviewed/200)ExpectedFrequencies(percent x 60)Dorm/fraternity 16 90 45 27Apartment/roominghouse, nearby13 40 20 12Apartment/roominghouse, distant16 40 20 12Live at home 15_____30_____15_____9_____Total 60 200 100 60
  101. 101. 17-101One-Sample Chi-SquareExampleNull Ho: 0 = EStatistical test One-sample chi-squareSignificance level .05Calculated value 9.89Critical test value 7.82(from Appendix C,Exhibit C-3)
  102. 102. 17-102Two-Sample Parametric Tests
  103. 103. 17-103Two-Sample t-Test ExampleA Group B GroupAveragehourly salesX1 =$1,500X2 =$1,300Standarddeviations1 = 225 s2 = 251
  104. 104. 17-104Two-Sample t-Test ExampleNull Ho: A sales = B salesStatistical test t-testSignificance level .05 (one-tailed)Calculated value 1.97, d.f. = 20Critical test value 1.725(from Appendix C,Exhibit C-2)
  105. 105. 17-105Two-Sample NonparametricTests: Chi-SquareOn-the-Job-AccidentCell DesignationCountExpected Values Yes No Row TotalSmokerHeavy Smoker1,112,8.241,247.7516Moderate2,197.732,267.2715Nonsmoker3,11318.033,22216.9735Column Total34 32 66
  106. 106. 17-106Two-Sample Chi-SquareExampleNull There is no difference indistribution channel for agecategories.Statistical test Chi-squareSignificance level .05Calculated value 6.86, d.f. = 2Critical test value 5.99(from Appendix C,Exhibit C-3)
  107. 107. 17-107SPSS Cross-TabulationProcedure
  108. 108. 17-108Two-Related-Samples TestsNonparametricParametric
  109. 109. 17-109Sales Data forPaired-Samples t-TestCompanySalesYear2SalesYear 1 Difference D D2GMGEExxonIBMFordAT&TMobilDuPontSearsAmocoTotal126932545748665662710961463611250220350995379423966123505496627894459512923003517348111324274997520779342749127712319238469392109263238193187ΣD = 35781 .1174432924127744594749441022720414971716881721444788169274241458476110156969ΣD = 157364693.
  110. 110. 17-110Paired-Samples t-Test ExampleNull Year 1 sales = Year 2 salesStatistical test Paired sample t-testSignificance level .01Calculated value 6.28, d.f. = 9Critical test value 3.25(from Appendix C,Exhibit C-2)
  111. 111. 17-111SPSS Output for Paired-Samples t-Test
  112. 112. 17-112Related Samples NonparametricTests: McNemar TestBeforeAfterDo Not FavorAfterFavorFavor A BDo Not Favor C D
  113. 113. 17-113Related Samples NonparametricTests: McNemar TestBeforeAfterDo Not FavorAfterFavorFavor A=10 B=90Do Not Favor C=60 D=40
  114. 114. 17-114k-Independent-Samples Tests:ANOVATests the null hypothesis that the means of threeor more populations are equalOne-way: Uses a single-factor, fixed-effectsmodel to compare the effects of a treatment orfactor on a continuous dependent variable
  115. 115. 17-115ANOVA Example__________________________________________ModelSummary_________________________________________Source d.f.Sum ofSquares Mean Square F Value p ValueModel (airline) 2 11644.033 5822.017 28.304 0.0001Residual (error) 57 11724.550 205.694Total 59 23368.583_______________________MeansTable________________________Count Mean Std. Dev. Std. ErrorLufthansa 20 38.950 14.006 3.132Malaysia Airlines 20 58.900 15.089 3.374Cathay Pacific 20 72.900 13.902 3.108
  116. 116. 17-116ANOVA Example ContinuedNull A1 = A2 = A3Statistical test ANOVA and F ratioSignificance level .05Calculated value 28.304, d.f. = 2, 57Critical test value 3.16(from AppendixC, Exhibit C-9)
  117. 117. 17-117Post Hoc: Scheffe’s S MultipleComparison ProcedureVerses DiffCrit.Diff. p ValueLufthansa MalaysiaAirlines19,950 11.400 .0002CathayPacific33.950 11.400 .0001MalaysiaAirlinesCathayPacific14.000 11.400 .0122
  118. 118. 17-118Multiple Comparison ProceduresTestComplexComparisonsPairwiseComparisonsEqualn’sOnlyUnequaln’sEqualVariancesAssumedUnequalVariancesNotAssumedFisher LSD X X XBonferroni X X XTukey HSD X X XTukey-Kramer X X XGames-Howell X X XTamhane T2 X X XScheffé S X X X XBrown-ForsytheX X X XNewman-Keuls X XDuncan X XDunnet’s T3 XDunnet’s C X
  119. 119. 17-119ANOVA PlotsLufthansaBusinessClassLounge
  120. 120. 17-120Two-Way ANOVA Example_______________________________________Model Summary___________________________Source d.f.Sum ofSquaresMeanSquare F Value p ValueAirline 2 11644.033 5822.017 39.178 0.0001Seat selection 1 3182.817 3182.817 21.418 0.0001Airline by seatselection2 517.033 258.517 1.740 0.1853Residual 54 8024.700 148.606All dataMeans Table Effect: Airline by Seat SelectionCount Mean Std. Dev. Std. ErrorLufthansaeconomy10 35.600 12.140 3.839Lufthansabusiness10 42.300 15.550 4.917Malaysia Airlineseconomy10 48.500 12.501 3.953Malaysia Airlinesbusiness10 69.300 9.166 2.898Cathay Pacificeconomy10 64.800 13.037 4.123Cathay Pacificbusiness10 81.000 9.603 3.037
  121. 121. 17-121k-Related-Samples TestsMore than two levels ingrouping factorObservations are matchedData are interval or ratio
  122. 122. 17-122Repeated-Measures ANOVAExampleAll data are hypothetical.___________________________________Means Table by Airline _________________________________________________________________________Count Mean Std. Dev. Std. ErrorRating 1, Lufthansa 20 38.950 14.006 3.132Rating 1, Malaysia Airlines 20 58.900 15.089 3.374Rating 1, Cathay Pacific 20 72.900 13.902 3.108Rating 2, Lufthansa 20 32.400 8.268 1.849Rating 2, Malaysia Airlines 20 72.250 10.572 2.364Rating 2, Cathay Pacific 20 79.800 11.265 2.519__________________________________________________________Model Summary_________________________________________________________Source d.f. Sum of Squares Mean Square F Value p ValueAirline 2 3552735.50 17763.775 67.199 0.0001Subject (group) 57 15067.650 264.345Ratings 1 625.633 625.633 14.318 0.0004Ratings by air....... 2 2061.717 1030.858 23.592 0.0001Ratings by subj..... 57 2490.650 43.696______________________________________Means Table Effect: Ratings_________________________________________________________________Count Mean Std. Dev. Std. ErrorRating 1 60 56.917 19.902 2.569Rating 2 60 61.483 23.208 2.996
  123. 123. 17-123Key Terms• a priori contrasts• Alternative hypothesis• Analysis of variance(ANOVA• Bayesian statistics• Chi-square test• Classical statistics• Critical value• F ratio• Inferential statistics• K-independent-samplestests• K-related-samples tests• Level of significance• Mean square• Multiple comparisontests (range tests)• Nonparametric tests• Normal probability plot
  124. 124. 17-124Key Terms• Null hypothesis• Observed significancelevel• One-sample tests• One-tailed test• p value• Parametric tests• Power of the test• Practical significance• Region of acceptance• Region of rejection• Statistical significance• t distribution• Trials• t-test• Two-independent-samples tests
  125. 125. 17-125Key Terms• Two-related-samplestests• Two-tailed test• Type I error• Type II error• Z distribution• Z test

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