Primer on linear models
- Boca Raton : Chapman & Hall 2008
- xiv, 287 p. ; ill., 23 cm
- Texts in statistical science .
Includes bibliographical references and index.
A Primer on Linear Models presents a unified, thorough, and rigorous development of the theory behind the statistical methodology of regression and analysis of variance (ANOVA). It seamlessly incorporates these concepts using non-full-rank design matrices and emphasizes the exact, finite sample theory supporting common statistical methods. This text enables complete comprehension of the material by taking a general, unifying approach to the theory, fundamentals, and exact results of linear models.
Linear models Aggregation, Gauss-Markov model ANOVA Best linear unbiased estimator Cell means model Cochran's theorem Distributional theory Eigenvalues Estimability First-order autoregressive error models Graam-Schmid orthonormalization Hypothesis testing Pythagorean theorem Multivariate linear model Nested model, two way Orthogonal polynomials Regression models Two-sample problem Unbiased estimators Zero estimator