Applied Univariate, Bivariate, and Multivariate Statistics. Daniel J. Denis
target="_blank" rel="nofollow" href="#ulink_b1e7751b-abc1-5204-a0a0-851abfbeac4e">10.7 EXPONENTIAL AND LOGARITHMIC FUNCTIONS 10.8 ODDS AND THE LOGIT 10.9 PUTTING IT ALL TOGETHER: LOGISTIC REGRESSION 10.10 LOGISTIC REGRESSION IN R 10.11 CHALLENGER ANALYSIS IN SPSS 10.12 SAMPLE SIZE, EFFECT SIZE, AND POWER 10.13 FURTHER DIRECTIONS 10.14 CHAPTER SUMMARY AND HIGHLIGHTS REVIEW EXERCISES
17 11 MULTIVARIATE ANALYSIS OF VARIANCE 11.1 A MOTIVATING EXAMPLE: QUANTITATIVE AND VERBAL ABILITY AS A VARIATE 11.2 CONSTRUCTING THE COMPOSITE 11.3 THEORY OF MANOVA 11.4 IS THE LINEAR COMBINATION MEANINGFUL? 11.5 MULTIVARIATE HYPOTHESES 11.6 ASSUMPTIONS OF MANOVA 11.7 HOTELLING’S T2: THE CASE OF GENERALIZING FROM UNIVARIATE TO MULTIVARIATE 11.8 THE COVARIANCE MATRIX S 11.9 FROM SUMS OF SQUARES AND CROSS‐PRODUCTS TO VARIANCES AND COVARIANCES 11.10 HYPOTHESIS AND ERROR MATRICES OF MANOVA 11.11 MULTIVARIATE TEST STATISTICS 11.12 EQUALITY OF COVARIANCE MATRICES 11.13 MULTIVARIATE CONTRASTS 11.14 MANOVA IN R AND SPSS 11.15 MANOVA OF FISHER’S IRIS DATA 11.16 POWER ANALYSIS AND SAMPLE SIZE FOR MANOVA 11.17 MULTIVARIATE ANALYSIS OF COVARIANCE AND MULTIVARIATE MODELS: A BIRD’S EYE VIEW OF LINEAR MODELS 11.18 CHAPTER SUMMARY AND HIGHLIGHTS REVIEW EXERCISES Further Discussion and Activities
18 12 DISCRIMINANT ANALYSIS 12.1 WHAT IS DISCRIMINANT ANALYSIS? THE BIG PICTURE ON THE IRIS DATA 12.2 THEORY OF DISCRIMINANT ANALYSIS 12.3 LDA IN R AND SPSS 12.4 DISCRIMINANT ANALYSIS FOR SEVERAL POPULATIONS 12.5 DISCRIMINATING SPECIES OF IRIS: DISCRIMINANT ANALYSES FOR THREE POPULATIONS 12.6 A NOTE ON CLASSIFICATION AND ERROR RATES 12.7 DISCRIMINANT ANALYSIS AND BEYOND 12.8 CANONICAL CORRELATION 12.9 MOTIVATING EXAMPLE FOR CANONICAL CORRELATION: HOTELLING’S 1936 DATA 12.10 CANONICAL CORRELATION AS A GENERAL LINEAR MODEL 12.11 THEORY OF CANONICAL CORRELATION 12.12 CANONICAL CORRELATION OF HOTELLING’S DATA 12.13 CANONICAL CORRELATION ON THE IRIS DATA: EXTRACTING CANONICAL CORRELATION FROM REGRESSION, MANOVA, LDA 12.14 CHAPTER SUMMARY AND HIGHLIGHTS REVIEW EXERCISES Further Discussion and Activities
19 13 PRINCIPAL COMPONENTS ANALYSIS 13.1 HISTORY OF PRINCIPAL COMPONENTS ANALYSIS 13.2 HOTELLING 1933 13.3 THEORY OF PRINCIPAL COMPONENTS ANALYSIS 13.4 EIGENVALUES AS VARIANCE 13.5 PRINCIPAL COMPONENTS AS LINEAR COMBINATIONS 13.6 EXTRACTING THE FIRST COMPONENT 13.7 EXTRACTING THE SECOND COMPONENT 13.8 EXTRACTING THIRD AND REMAINING COMPONENTS 13.9 THE EIGENVALUE AS THE VARIANCE OF A LINEAR COMBINATION RELATIVE TO ITS LENGTH 13.10 DEMONSTRATING PRINCIPAL COMPONENTS ANALYSIS: PEARSON’S 1901 ILLUSTRATION 13.11 SCREE PLOTS 13.12 PRINCIPAL COMPONENTS VERSUS LEAST‐SQUARES REGRESSION LINES 13.13 COVARIANCE VERSUS CORRELATION MATRICES: PRINCIPAL COMPONENTS AND SCALING 13.14 PRINCIPAL COMPONENTS ANALYSIS USING SPSS 13.15 CHAPTER SUMMARY AND HIGHLIGHTS REVIEW EXERCISES Further Discussion and Activities
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14 FACTOR ANALYSIS
14.1 HISTORY OF FACTOR ANALYSIS
14.2 FACTOR ANALYSIS AT A GLANCE
14.3 EXPLORATORY VERSUS CONFIRMATORY FACTOR