Applied Multivariate Statistics with SAS Software, Second Edition

ISBN: 978-0-471-32299-3 公卫人
Paperback
360 pages
April 1999
Wiley List Price: US $78.50

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

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* 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. 公卫人

Preface
Commonly Used Notation
Chapter 1: Multivariate Analysis Concepts
Introduction
Random Vectors, Means, Variances, and Covariances
Multivariate Normal Distribution
Sampling from Multivariate Normal Populations

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Some Important Sample Statistics and Their Distributions
Tests for Multivariate Normality
Random Vector and Matrix Generation
Chapter 2: Graphical Representation of Multivariate Data
Introduction
Scatter Plots
Profile Plots
Andrews Function Plots
Biplots: Plotting Observations and Variables Together
Q-Q Plots for Assessing Multivariate Normality
Plots for Detection of Multivariate Outliers
Bivariate Normal Distribution
SAS/INSIGHT Software
Concluding Remarks
Chapter 3: Multivariate Regression

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Introduction
Statistical Background
Least Squares Estimation
ANOVA Partitioning
Testing Hypotheses: Linear Hypotheses
Simultaneous Confidence Intervals
Multiple Response Surface Modeling
General Linear Hypotheses
Variance and Bias Analyses for Calibration Problems
Regression Diagnostics
Concluding Remarks
Chapter 4: Multivariate Analysis of Experimental Data
Introduction
Balanced and Unbalanced Data
One-Way Classification
Two-Way Classification
Blocking
Fractional Factorial Experiments 公卫考场
Analysis of Covariance
Concluding Remarks
Chapter 5: Analysis of Repeated Measures Data
Introduction
Single Population
k Populations
Factorial Designs
Analysis in the Presence of Covariates
The Growth Curve Models
Crossover Designs
Concluding Remarks
Chapter 6: Analysis of Repeated Measures Using Mixed Models
Introduction
The Mixed Effects Linear Model
An Overview of the MIXED Procedure
Statistical Tests for Covariance Structures
Models with Only Fixed Effects

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Analysis in the Presence of Covariates
A Random Coefficient Model
Multivariate Repeated Measures Data
Concluding Remarks
References
Appendix A: A Brief Introduction to the IML Procedure
The First SAS Statement
Scalars
Matrices
Printing of Matrices
Algebra of Matrices
Transpose
Inverse
Finding the Number of Rows and Columns
Trace and Determinant
Eigenvalues and Eigenvectors
Square Root of a Symmetric Nonnegative Definite Matrix
Generalized Inverse of a Matrix 公卫考场
Singular Value Decomposition
Symmetric Square Root of a Symmetric Nonnegative Definite Matrix
Kronecker Product
Augmenting Two or More Matrices
Construction of a Design Matrix
Checking the Estimability of a Linear Function p' B
Creating a Matrix from a SAS Data Set 公卫百科
Creating a SAS Data Set from a Matrix
Generation of Normal Random Numbers
Computation of Cumulative Probabilities
Computation of Percentiles and Cut Off Points
Appendix B: Data Sets
Index

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