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5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
5 data analysis approaches dr. hueihsia holloman
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5 data analysis approaches dr. hueihsia holloman

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  • 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. 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. 15-3PulsePoint:Research Revelation55 The percent of white-collar workerswho answer work-related calls or e-mail after work hours.
  • 4. 15-4Data Preparationin the Research Process
  • 5. 15-5MonitoringOnline Survey DataOnline surveys needspecial editing attention.CfMC provides softwareand support to researchsuppliers to preventinterruptions fromdamaging data .
  • 6. 15-6EditingCriteriaConsistentUniformlyenteredArranged forsimplificationCompleteAccurate
  • 7. 15-7Field EditingSpeed without accuracy won’thelp the manager choose theright direction.•Field editing review•Entry gaps identified•Callbacks made•Validate results
  • 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. 15-9Sample Codebook
  • 10. 15-10Precoding
  • 11. 15-11CodingOpen-Ended Questions6. What prompted you to purchase yourmost recent life insurance policy?________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
  • 12. 15-12Coding RulesCategoriesshould beAppropriate to theresearch problemExhaustiveMutually exclusiveDerived from oneclassification principle
  • 13. 15-13Content AnalysisQSR’s XSightsoftware forcontent analysis.
  • 14. 15-14Content Analysis
  • 15. 15-15Types of Content AnalysisSyntacticalPropositionalReferentialThematic
  • 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. 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. 15-18Data EntryDatabaseProgramsOpticalRecognitionDigital/BarcodesVoicerecognitionKeyboarding
  • 19. 15-19Missing DataListwise DeletionPairwise DeletionReplacement
  • 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. Appendix 15aDescribing DataStatisticallyMcGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
  • 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. 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. 15-24Distributions
  • 25. 15-25Characteristics of Distributions
  • 26. 15-26Measures of Central TendencyMean ModeMedian
  • 27. 15-27Measures of VariabilityInterquartilerangeQuartiledeviationRangeStandarddeviationVariance
  • 28. 15-28Summarizing Distribution Shape
  • 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. 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. 15-31
  • 32. Chapter 16Exploring, Displaying,and Examining DataMcGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
  • 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. 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. 16-35PulsePoint:Research Revelation65 The percent boost in companyrevenue created by best practices indata quality.
  • 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. 16-37Exploratory Data AnalysisConfirmatoryExploratory
  • 38. 16-38Data Exploration, Examination,and Analysis in the ResearchProcess
  • 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. 16-40Frequency of Ad RecallValue Label Value Frequency Percent Valid CumulativePercent Percent
  • 41. 16-41Bar Chart
  • 42. 16-42Pie Chart
  • 43. 16-43Frequency Table
  • 44. 16-44Histogram
  • 45. 16-45Stem-and-Leaf Display45566678888912466799022356780226824018310633636856789101112131415161718192021
  • 46. 16-46Pareto Diagram
  • 47. 16-47Boxplot Components
  • 48. 16-48Diagnostics with Boxplots
  • 49. 16-49Boxplot Comparison
  • 50. 16-50Mapping
  • 51. 16-51Geograph: Digital Camera Ownership
  • 52. 16-52SPSS Cross-Tabulation
  • 53. 16-53Percentages inCross-Tabulation
  • 54. 16-54Guidelines for Using PercentagesAveraging percentagesUse of too large percentagesUsing too small a basePercentage decreases cannever exceed 100%
  • 55. 16-55Cross-Tabulation with Controland Nested Variables
  • 56. 16-56Automatic Interaction Detection(AID)
  • 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. 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. Working withData TablesMcGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
  • 60. 16-60Original Data TableOur grateful appreciation to eMarketer for
  • 61. 16-61Arranged by Spending
  • 62. 16-62Arranged byNo. of Purchases
  • 63. 16-63Arranged by Avg. Transaction,Highest
  • 64. 16-64Arranged by Avg. Transaction,Lowest
  • 65. 15-65
  • 66. Chapter 17Hypothesis TestingMcGraw-Hill/Irwin Copyright © 2011 by The McGraw-Hill Companies, Inc. All Rights Reserved.
  • 67. 17-67Learning ObjectivesUnderstand . . .• The nature and logic of hypothesis testing.• A statistically significant difference• The six-step hypothesis testing procedure.
  • 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. 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. 17-70PulsePoint:Research Revelation$28 The amount, in billions, saved byNorth American companies byhaving employees use a companypurchasing card.
  • 71. 17-71Hypothesis TestingDeductiveReasoningInductiveReasoning
  • 72. 17-72Hypothesis Testing Finds Truth“One finds the truth by making ahypothesis and comparing the truth tothe hypothesis.”David DouglassphysicistUniversity of Rochester
  • 73. 17-73Statistical ProceduresDescriptiveStatisticsInferentialStatistics
  • 74. 17-74Hypothesis Testingand the Research Process
  • 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. 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. 17-77Statistical Significance
  • 78. 17-78Types of HypothesesNull– H0:  = 50 mpg– H0:  < 50 mpg– H0:  > 50 mpgAlternate– HA:  = 50 mpg– HA:  > 50 mpg– HA:  < 50 mpg
  • 79. 17-79Two-Tailed Test of Significance
  • 80. 17-80One-Tailed Test of Significance
  • 81. 17-81Decision RuleTake no corrective action if theanalysis shows that one cannotreject the null hypothesis.
  • 82. 17-82Statistical Decisions
  • 83. 17-83Probability of Making a Type IError
  • 84. 17-84Critical Values
  • 85. 17-85Exhibit 17-4 Probability ofMaking A Type I Error
  • 86. 17-86Factors Affecting Probability ofCommitting a  ErrorTrue value of parameterAlpha level selectedOne or two-tailed test usedSample standard deviationSample size
  • 87. 17-87Probability of Making A Type IIError
  • 88. 17-88Statistical TestingProceduresObtain criticaltest valueInterpret thetestStagesChoosestatistical testState nullhypothesisSelect level ofsignificanceComputedifferencevalue
  • 89. 17-89Tests of SignificanceNonparametricParametric
  • 90. 17-90Assumptions for UsingParametric TestsIndependent observationsNormal distributionEqual variancesInterval or ratio scales
  • 91. 17-91Probability Plot
  • 92. 17-92Probability Plot
  • 93. 17-93Probability Plot
  • 94. 17-94Advantages of NonparametricTestsEasy to understand and useUsable with nominal dataAppropriate for ordinal dataAppropriate for non-normalpopulation distributions
  • 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. 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. 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. 17-98Parametric Testst-testZ-test
  • 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. 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. 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. 17-102Two-Sample Parametric Tests
  • 103. 17-103Two-Sample t-Test ExampleA Group B GroupAveragehourly salesX1 =$1,500X2 =$1,300Standarddeviations1 = 225 s2 = 251
  • 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. 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. 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. 17-107SPSS Cross-TabulationProcedure
  • 108. 17-108Two-Related-Samples TestsNonparametricParametric
  • 109. 17-109Sales Data forPaired-Samples t-TestCompanySalesYear2SalesYear 1 Difference D D2GMGEExxonIBMFordAT&TMobilDuPontSearsAmocoTotal126932545748665662710961463611250220350995379423966123505496627894459512923003517348111324274997520779342749127712319238469392109263238193187ΣD = 35781 .1174432924127744594749441022720414971716881721444788169274241458476110156969ΣD = 157364693.
  • 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. 17-111SPSS Output for Paired-Samples t-Test
  • 112. 17-112Related Samples NonparametricTests: McNemar TestBeforeAfterDo Not FavorAfterFavorFavor A BDo Not Favor C D
  • 113. 17-113Related Samples NonparametricTests: McNemar TestBeforeAfterDo Not FavorAfterFavorFavor A=10 B=90Do Not Favor C=60 D=40
  • 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. 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. 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. 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. 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. 17-119ANOVA PlotsLufthansaBusinessClassLounge
  • 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. 17-121k-Related-Samples TestsMore than two levels ingrouping factorObservations are matchedData are interval or ratio
  • 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. 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. 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. 17-125Key Terms• Two-related-samplestests• Two-tailed test• Type I error• Type II error• Z distribution• Z test

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