Applied multivariate statistics with SAS software

Identity, Politics and the Novel is a diverse and wide-ranging book that offers an innovative and unique approach to several works by four critically acclaimed novelists: Milan Kundera, Ian McEwan, Michel Houellebecq, and J. M. Coetzee. Drawing from classical and contemporary political, philosophical, and social theory--including foundational texts by Adorno, Aquinas, Camus, Hegel, and Nietzsche--Ian Fraser tracks these novelists' use of the aesthetic self and, in turn, develops the notion of a Marxist aesthetic identity through the medium of contemporary fiction.

Author Notes

Ravindra Khattree, professor of applied statistics at Oakland University, Rochester, Michigan
Dayanand N. Naik is an associate professor of statistics at Old Dominion University, Norfolk, Virginia

Preface	p. ix
Commonly Used Notation	p. xiii
1 Multivariate Analysis Concepts	p. 1
1.1 Introduction	p. 1
1.2 Random Vectors, Means, Variances, and Covariances	p. 2
1.3 Multivariate Normal Distribution	p. 5
1.4 Sampling from Multivariate Normal Populations	p. 6
1.5 Some Important Sample Statistics and Their Distributions	p. 8
1.6 Tests for Multivariate Normality	p. 9
1.7 Random Vector and Matrix Generation	p. 17
2 Graphical Representation of Multivariate Data	p. 21
2.1 Introduction	p. 21
2.2 Scatter Plots	p. 22
2.3 Profile Plots	p. 31
2.4 Andrews Function Plots	p. 33
2.5 Biplots: Plotting Observations and Variables Together	p. 38
2.6 Q-Q Plots for Assessing Multivariate Normality	p. 45
2.7 Plots for Detection of Multivariate Outliers	p. 50
2.8 Bivariate Normal Distribution	p. 53
2.9 SAS/INSIGHT Software	p. 58
2.10 Concluding Remarks	p. 59
3 Multivariate Regression	p. 61
3.1 Introduction	p. 61
3.2 Statistical Background	p. 62
3.3 Least Squares Estimation	p. 63
3.4 ANOVA Partitioning	p. 64
3.5 Testing Hypotheses: Linear Hypotheses	p. 66
3.6 Simultaneous Confidence Intervals	p. 84
3.7 Multiple Response Surface Modeling	p. 87
3.8 General Linear Hypotheses	p. 91
3.9 Variance and Bias Analyses for Calibration Problems	p. 98
3.10 Regression Diagnostics	p. 102
3.11 Concluding Remarks	p. 116
4 Multivariate Analysis of Experimental Data	p. 117
4.1 Introduction	p. 117
4.2 Balanced and Unbalanced Data	p. 120
4.3 One-Way Classification	p. 123
4.4 Two-Way Classification	p. 129
4.5 Blocking	p. 137
4.6 Fractional Factorial Experiments	p. 139
4.7 Analysis of Covariance	p. 145
4.8 Concluding Remarks	p. 149
5 Analysis of Repeated Measures Data	p. 151
5.1 Introduction	p. 151
5.2 Single Population	p. 152
5.3 k Populations	p. 176
5.4 Factorial Designs	p. 195
5.5 Analysis in the Presence of Covariates	p. 207
5.6 The Growth Curve Models	p. 219
5.7 Crossover Designs	p. 236
5.8 Concluding Remarks	p. 246
6 Analysis of Repeated Measures Using Mixed Models	p. 247
6.1 Introduction	p. 247
6.2 The Mixed Effects Linear Model	p. 248
6.3 An Overview of the MIXED Procedure	p. 252
6.4 Statistical Tests for Covariance Structures	p. 255
6.5 Models with Only Fixed Effects	p. 265
6.6 Analysis in the Presence of Covariates	p. 274
6.7 A Random Coefficient Model	p. 288
6.8 Multivariate Repeated Measures Data	p. 294
6.9 Concluding Remarks	p. 297
References	p. 299
Appendix A A Brief Introduction to the IML Procedure	p. 305
A.1 The First SAS Statement	p. 305
A.2 Scalars	p. 305
A.3 Matrices	p. 305
A.4 Printing of Matrices	p. 306
A.5 Algebra of Matrices	p. 306
A.6 Transpose	p. 306
A.7 Inverse	p. 306
A.8 Finding the Number of Rows and Columns	p. 307
A.9 Trace and Determinant	p. 307
A.10 Eigenvalues and Eigenvectors	p. 307
A.11 Square Root of a Symmetric Nonnegative Definite Matrix	p. 308
A.12 Generalized Inverse of a Matrix	p. 308
A.13 Singular Value Decomposition	p. 309
A.14 Symmetric Square Root of a Symmetric Nonnegative Definite Matrix	p. 309
A.15 Kronecker Product	p. 309
A.16 Augmenting Two or More Matrices	p. 310
A.17 Construction of a Design Matrix	p. 310
A.18 Checking the Estimability of a Linear Function p'[beta]	p. 311
A.19 Creating a Matrix from a SAS Data Set	p. 312
A.20 Creating a SAS Data Set from a Matrix	p. 312
A.21 Generation of Normal Random Numbers	p. 312
A.22 Computation of Cumulative Probabilities	p. 313
A.23 Computation of Percentiles and Cut Off Points	p. 313
Appendix B Data Sets	p. 315
Index	p. 327

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Author Notes

Table of Contents