| Description |
xxiv, 693 pages |
| Series |
Springer texts in statistics |
|
Springer texts in statistics.
|
| Contents |
Machine derived contents note: Introduction Vectors and Matrices Multivariate Distributions and the Linear Model Multivariate Regression Models Seemingly Unrelated Regression Models Multivariate Random and Mixed Models Discriminant and Classification Analysis Principal Component, Canonical Correlation, and Exploratory Factor Analysis Cluster Analysis and Multidimensional Scaling Structural Equation Models |
| Summary |
Univariate statistical analysis is concerned with techniques for the analysis of a single random variable. This book is about applied multivariate analysis. It was written to provide students and researchers with an introduction to statistical techniques for the analysis of continuous quantitative measurements on several random variables simultaneously. While quantitative measurements may be obtained from any population, the material in this text is primarily concerned with techniques useful for the analysis of continuous observations from multivariate normal populations with linear structure. While several multivariate methods are extensions of univariate procedures, a unique feature of multivariate data analysis techniques is their ability to control experimental error at an exact nominal level and to provide information on the covariance structure of the data. These features tend to enhance statistical inference, making multivariate data analysis superior to univariate analysis. -- preface |
| Notes |
Formerly CIP. Uk |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Also available electronically via WWW |
|
English |
| Subject |
Multivariate analysis.
|
| LC no. |
2001049267 |
| ISBN |
0387953477 (alk. paper) |
|
