- Linear Models In Statistics Rencher
- Regression Linear Models In Statistics
- General Linear Models In Statistics
- Linear Models In Statistics
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.
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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:
Linear Models In Statistics Rencher
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
Regression Linear Models In Statistics
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.
General Linear Models In Statistics
Linear Models in Statistics Alvin C. Linear Models in Statistics. Rencher's Linear Model in Statistics provides a good overview of regression and ANOVA theory. The essential introduction to the theory and application of linear models. Linear Models in Statistics. Rencher, PhD, is Professor of Statistics. LINEAR MODELS IN STATISTICS Second Edition Alvin C. Bruce Schaalje Department of Statistics, Brigham Young University, Provo, Utah. 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 cells,geometry of least squares, vector-matrix calculus, simultaneousinference, and logistic and nonlinear regression.
Linear Models In Statistics
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.