This updated textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The author's emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models.

Features exercises throughout, with additional exercises supplied at the end of each chapter so that readers can retain theory Illustrates the practical application of the projective approach to linear models Includes appendices that with prerequisite background information on linear algebra and mathematical statistics Prepared in conjunction with a new edition of Christensen's Advanced Linear Modeling, so that advanced undergraduate and graduate students have access to a wealth of revised content in statistical theory Provides access to accompanying computer code Includes supplementary material: sn.pub/extras

Autorentext

Ronald Christensen is a Professor of Statistics at the University of New Mexico, Fellow of the American Statistical Association (ASA) and the Institute of Mathematical Statistics, former Chair of the ASA Section on Bayesian Statistical Science and former Editor of The American Statistician. His book publications include Advanced Linear Modeling (Springer, new edition forthcoming), Log-Linear Models and Logistic Regression (Springer 1997), Analysis of Variance, Design, and Regression (1996, 2016), and Bayesian Ideas and Data Analysis (2010, with Johnson, Branscum and Hanson).

Inhalt

1. Introduction.- 2. Estimation.- 3. Testing.- 4. One-Way ANOVA.- 5. Multiple Comparison Techniques.- 6. Regression Analysis.- 7. Multifactor Analysis of Variance.- 8. Experimental Design Models.- 9. Analysis of Covariance.- 10. General Gauss-Markov Models.- 11. Split Plot Models.- 12. Model Diagnostics.- 13. Collinearity and Alternative Estimates.- 14. Variable Selection.- Appendix A - 6.- References.- Index.- Author Index.