Linear models made easy with this unique introduction Linear Models in Statistics discusses classical linear models from a matrix algebra perspective, making the subject easily accessible to readers encountering linear models for the first time. It provides a solid foundation from which to explore the literature and interpret correctly the output of computer packages, and brings together a number of approaches to regression and analysis of variance that more experienced practitioners will also benefit from. With an emphasis on broad coverage of essential topics, Linear Models in Statistics carefully develops the basic theory of regression and analysis of variance, illustrating it with examples from a wide range of disciplines. Other features of this remarkable work include:
* Easy-to-read proofs and clear explanations of concepts and procedures
* Special topics such as multiple regression with random x's and the effect of each variable on R�2
* Advanced topics such as mixed and generalized linear models as well as logistic and nonlinear regression
* The use of real data sets in examples, with all data sets available over the Internet
* Numerous theoretical and applied problems, with answers in an appendix
* A thorough review of the requisite matrix algebra
* Graphs, charts, and tables as well as extensive references

About the Author

ALVIN C. RENCHER, PhD, is Professor of Statistics at Brigham Young University and a Fellow of the American Statistical Association. He is the author of Methods of Multivariate Analysis and Multivariate Statistical Inference and Applications, both available from Wiley.

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