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Linear Models in Statistics

By Rencher, Alvin C.

Linear Models in Statistics Mathematics

Linear Models in Statistics

By Rencher, Alvin C.

0 - Default Title
Description
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 ysis 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 ysis 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
Product details
Edition:
1
Number of Pages:
578
Release Date:
1999-10-01
Publication Date:
1999-10-01
Publisher:
John Wiley & Sons Inc
Languages:
Published: English, Original: English
ISBN10:
0471315648
GPSR Manufacturer Reference:
[email protected]
Weight:
1021 g
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