The essential introduction to the theory and application of linear models now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.

The essential introduction to the theory and application of linearmodels--now in a valuable new editionSince most advanced statistical tools are generalizations of thelinear model, it is neces-sary to first master the linear model inorder to move forward to more advanced concepts. The linear modelremains the main tool of the applied statistician and is central tothe training of any statistician regardless of whether the focus isapplied or theoretical. This completely revised and updated newedition successfully develops the basic theory of linear models forregression, analysis of variance, analysis of covariance, andlinear mixed models. Recent advances in the methodology related tolinear mixed models, generalized linear models, and the Bayesianlinear model are also addressed.Linear Models in Statistics, Second Edition includes fullcoverage of advanced topics, such as mixed and generalized linearmodels, Bayesian linear models, two-way models with empty cellsgeometry of least squares, vector-matrix calculus, simultaneousinference, and logistic and nonlinear regression. Algebraicgeometrical, frequentist, and Bayesian approaches to both theinference of linear models and the analysis of variance are alsoillustrated. Through the expansion of relevant material and theinclusion of the latest technological developments in the fieldthis book provides readers with the theoretical foundation tocorrectly interpret computer software output as well as effectivelyuse, customize, and understand linear models.This modern Second Edition features:* New chapters on Bayesian linear models as well as random andmixed linear models* Expanded discussion of two-way models with empty cells* Additional sections on the geometry of least squares* Updated coverage of simultaneous inferenceThe book is complemented with easy-to-read proofs, real datasets, and an extensive bibliography. A thorough review of therequisite matrix algebra has been addedfor transitional purposesand numerous theoretical and applied problems have beenincorporated with selected answers provided at the end of the book.A related Web site includes additional data sets and SAS® codefor all numerical examples.Linear Model in Statistics, Second Edition is a must-have bookfor courses in statistics, biostatistics, and mathematics at theupper-undergraduate and graduate levels. It is also an invaluablereference for researchers who need to gain a better understandingof regression and analysis of variance.

Preface.1. Introduction.2. Matrix Algebra.3. Random Vectors and Matrices.4. Multivariate Normal Distribution.5. Distribution of Quadratic Forms in y.6. Simple Linear Regression.7. Multiple Regression: Estimation.8. Multiple Regression: tests of Hypotheses and ConfidenceIntervals.9. Multiple Regression: Model Validation and Diagnostics.10. Multiple Regression: random x's.11. Multiple Regression: Bayesian Inference.12. Analysis-of-Variance Models.13. One-Way Analysis-of-Variance: balanced Case.14. Two-Way Analysis-of Variance: Balanced Case.15. Analysis-of-Variance: The Cell Means Model for UnbalancedData.16. Analysis-of-Covariance.17. Linear Mixed Models.18. Additional Models.Appendix A. Answers and Hits to the Problems.References.Index.