Linear models in statistics
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Bibliographic Information
Linear models in statistics
（Wiley series in probability and mathematical statistics）
Wiley, c2000
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Includes bibliographical references and index
Description and Table of Contents
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 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
Table of Contents
Matrix Algebra. Random Vectors and Matrices. Multivariate Normal Distribution. Distribution of Quadratic Forms in y. Simple Linear Regression. Multiple Regression: Estimation. Multiple Regression: Tests of Hypotheses and Confidence Intervals. Multiple Regression: Model Validation and Diagnostics. Multiple Regression: Random x's. Analysis of Variance Models. One--Way Analysis of Variance: Balanced Case. Two--Way Analysis of Variance: Balanced Case. Analysis of Variance: Unbalanced Data. Analysis of Covariance. Random Effects Models and Mixed Effects Models. Additional Models. Answers and Hints to Selected Problems. Data Sets and SAS Files. Bibliography.
by "Nielsen BookData"