Applied multivariate statistical analysis (Prentice Hall series in statistics)

This Book Is Concerned With Statistical Methods For Describing And Analyzing Multivariate Data. The Authors Hope That Their Discussions Will Meet The Needs Of Experimental Scientists, In A Wide Variety Of Subject Matter Areas, As A Readable Introduciton To The Staistical Analysis Of Multvariate Observations. The Authors Emphasize The Applications Of Multivariate Methods And, Have Attempted To Make The Mathematics As Palatable As Possible. Part 1: Getting Started -- Chapter 1: Aspects Of Multivariate Analysis -- Chapter 2: Matrix Algebra And Random Vectors -- Chapter 3: Sample Geometry And Random Sampling -- Chapter 4: The Multivariate Normal Distribution -- Chapter 5: Inferences About A Mean Vector -- Chapter 6: Comparisons Of Several Multivariate Means -- Chapter 7: Multivariate Linear Regression Models -- Part 3: Analysis Of Covariance Structure -- Chapter 8: Principal Components -- Chapter 9: Factor Analysis And Inference For Structured Covariance Matrices -- Chapter 10: Canonical Correlation Analysis -- Part 4: Classification And Grouping Techniques -- Chapter 11: Discrimination And Classification -- Chapter 12: Clustering. Richard A. Johnson, Dean W. Wichern. Includes Bibliographies And Indexes.