Linear Models: The Theory and Application of Analysis of Variance (Repost)

An insightful approach to the analysis of variance in the study oflinear models
Linear Models explores the theory of linear models and thedynamic relationships that these models have with Analysis ofVariance (ANOVA), experimental design, and random and mixed-modeleffects. This one-of-a-kind book emphasizes an approach thatclearly explains the distribution theory of linear models andexperimental design starting from basic mathematical concepts inlinear algebra.
The author begins with a presentation of the classicfixed-effects linear model and goes on to illustrate eight commonlinear models, along with the value of their use in statistics.From this foundation, subsequent chapters introduce conceptspertaining to the linear model, starting with vector space theoryand the theory of least-squares estimation. An outline of theHelmert matrix is also presented, along with a thorough explanationof how the ANOVA is created in both typical two-way and higherlayout designs, ultimately revealing the distribution theory. Otherimportant topics covered include:
- Vector space theory
- The theory of least squares estimation
- Gauss-Markov theorem
- Kronecker products
- Diagnostic and robust methods for linear models
- Likelihood approaches to estimation
A discussion of Bayesian theory is also included for purposes ofcomparison and contrast, and numerous illustrative exercises assistthe reader with uncovering the nature of the models, using bothclassic and new data sets. Requiring only a working knowledge ofbasic probability and statistical inference, Linear Models is avaluable book for courses on linear models at theupper-undergraduate and graduate levels. It is also an excellentreference for practitioners who use linear models to conductresearch in the fields of econometrics, psychology, sociology,biology, and agriculture.