Catalogue


Applied multivariate statistics with SAS software /
Ravindra Khattree, Dayanand N. Naik.
edition
2nd ed.
imprint
Cary, NC : SAS Institute, c1999.
description
xiv, 338 p. : ill. ; 28 cm.
ISBN
0471322997
format(s)
Book
Holdings
More Details
added author
imprint
Cary, NC : SAS Institute, c1999.
isbn
0471322997
catalogue key
3928590
 
Includes bibliographical references and index.
A Look Inside
About the Author
Author Affiliation
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
Summaries
Main Description
Real-world problems and data sets are the backbone of this groundbreaking book. Applied Multivariate Statistics with SAS? Software, Second Edition provides a unique approach to this topic, integrating statistical methods, data analysis, and applications. Now extensively revised, the book includes new information on * mixed effects models * applications of the MIXED procedure * regression diagnostics with the correspoding IML procedure code * covariance structures. The authors' approach to the information aids professors, researchers, and students in a variety of disciplines and industries. Extensive SAS code and the corresponding output accompany sample problems, and clear explanations of the various SAS procedures are included. Emphasis is on correct interpretation of the output to draw meaningful conclusions. Featuring both the theoretical and the practical, topics covered include multivariate analysis of experimental data and repeated measures data, graphical representation of data including biplots, and multivariate regression. In addition, a quick introduction to the IML procedure with special reference to multivariate data is available in an appendix. SAS programs and output integrated with the text make it easy to read and follow the examples. High-resolution graphs have been used in this new edition.
Main Description
Real-world problems and data sets are the backbone of this groundbreaking book. Applied Multivariate Statistics with SASĀ® Software, Second Edition provides a unique approach to this topic, integrating statistical methods, data analysis, and applications. Now extensively revised, the book includes new information on * mixed effects models * applications of the MIXED procedure * regression diagnostics with the correspoding IML procedure code * covariance structures. The authors' approach to the information aids professors, researchers, and students in a variety of disciplines and industries. Extensive SAS code and the corresponding output accompany sample problems, and clear explanations of the various SAS procedures are included. Emphasis is on correct interpretation of the output to draw meaningful conclusions. Featuring both the theoretical and the practical, topics covered include multivariate analysis of experimental data and repeated measures data, graphical representation of data including biplots, and multivariate regression. In addition, a quick introduction to the IML procedure with special reference to multivariate data is available in an appendix. SAS programs and output integrated with the text make it easy to read and follow the examples. High-resolution graphs have been used in this new edition.
Back Cover Copy
Real-world problems and data sets are the backbone of this groundbreaking book. Applied Multivariate Statistics with SASreg; Software, Second Edition provides a unique approach to this topic, integrating statistical methods, data analysis, and applications. Now extensively revised, the book includes new information on mixed effects models applications of the MIXED procedure regression diagnostics with the correspoding IML procedure code covariance structures. The authors' approach to the information aids professors, researchers, and students in a variety of disciplines and industries. Extensive SAS code and the corresponding output accompany sample problems, and clear explanations of the various SAS procedures are included. Emphasis is on correct interpretation of the output to draw meaningful conclusions. Featuring both the theoretical and the practical, topics covered include multivariate analysis of experimental data and repeated measures data, graphical representation of data including biplots, and multivariate regression. In addition, a quick introduction to the IML procedure with special reference to multivariate data is available in an appendix. SAS programs and output integrated with the text make it easy to read and follow the examples. High-resolution graphs have been used in this new edition.
Table of Contents
Prefacep. ix
Commonly Used Notationp. xiii
Multivariate Analysis Conceptsp. 1
Introductionp. 1
Random Vectors, Means, Variances, and Covariancesp. 2
Multivariate Normal Distributionp. 5
Sampling from Multivariate Normal Populationsp. 6
Some Important Sample Statistics and Their Distributionsp. 8
Tests for Multivariate Normalityp. 9
Random Vector and Matrix Generationp. 17
Graphical Representation of Multivariate Datap. 21
Introductionp. 21
Scatter Plotsp. 22
Profile Plotsp. 31
Andrews Function Plotsp. 33
Biplots: Plotting Observations and Variables Togetherp. 38
Q-Q Plots for Assessing Multivariate Normalityp. 45
Plots for Detection of Multivariate Outliersp. 50
Bivariate Normal Distributionp. 53
SAS/INSIGHT Softwarep. 58
Concluding Remarksp. 59
Multivariate Regressionp. 61
Introductionp. 61
Statistical Backgroundp. 62
Least Squares Estimationp. 63
ANOVA Partitioningp. 64
Testing Hypotheses: Linear Hypothesesp. 66
Simultaneous Confidence Intervalsp. 84
Multiple Response Surface Modelingp. 87
General Linear Hypothesesp. 91
Variance and Bias Analyses for Calibration Problemsp. 98
Regression Diagnosticsp. 102
Concluding Remarksp. 116
Multivariate Analysis of Experimental Datap. 117
Introductionp. 117
Balanced and Unbalanced Datap. 120
One-Way Classificationp. 123
Two-Way Classificationp. 129
Blockingp. 137
Fractional Factorial Experimentsp. 139
Analysis of Covariancep. 145
Concluding Remarksp. 149
Analysis of Repeated Measures Datap. 151
Introductionp. 151
Single Populationp. 152
k Populationsp. 176
Factorial Designsp. 195
Analysis in the Presence of Covariatesp. 207
The Growth Curve Modelsp. 219
Crossover Designsp. 236
Concluding Remarksp. 246
Analysis of Repeated Measures Using Mixed Modelsp. 247
Introductionp. 247
The Mixed Effects Linear Modelp. 248
An Overview of the MIXED Procedurep. 252
Statistical Tests for Covariance Structuresp. 255
Models with Only Fixed Effectsp. 265
Analysis in the Presence of Covariatesp. 274
A Random Coefficient Modelp. 288
Multivariate Repeated Measures Datap. 294
Concluding Remarksp. 297
Referencesp. 299
A Brief Introduction to the IML Procedurep. 305
The First SAS Statementp. 305
Scalarsp. 305
Matricesp. 305
Printing of Matricesp. 306
Algebra of Matricesp. 306
Transposep. 306
Inversep. 306
Finding the Number of Rows and Columnsp. 307
Trace and Determinantp. 307
Eigenvalues and Eigenvectorsp. 307
Square Root of a Symmetric Nonnegative Definite Matrixp. 308
Generalized Inverse of a Matrixp. 308
Singular Value Decompositionp. 309
Symmetric Square Root of a Symmetric Nonnegative Definite Matrixp. 309
Kronecker Productp. 309
Augmenting Two or More Matricesp. 310
Construction of a Design Matrixp. 310
Checking the Estimability of a Linear Function p'[beta]p. 311
Creating a Matrix from a SAS Data Setp. 312
Creating a SAS Data Set from a Matrixp. 312
Generation of Normal Random Numbersp. 312
Computation of Cumulative Probabilitiesp. 313
Computation of Percentiles and Cut Off Pointsp. 313
Data Setsp. 315
Indexp. 327
Table of Contents provided by Ingram. All Rights Reserved.

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