From the reviews: The present book is intended for a second undergraduate or beginning graduate course in statistics providing further study of this single topic. … Complete, mathematically rigorous proofs are routinely provided for theorems. The fully-worked examples and solutions to the exercises are detailed. … Linear Models in Statistics is highly suitable for a theoretical statistics course for advanced undergraduate math majors, beginning math graduate students or others interested in using the book for independent study. (Susan D'Agostino, The Mathematical Association of America, December, 2010) Intended primarily for advanced undergraduate and beginning graduate students with knowledge of the basic concepts of statistics, probability, and linear algebra, this student-friendly book provides a lucid presentation of numerous regression analysis topics. … A salient feature is the numerous, carefully selected worked examples and complete solutions to all the problems in various chapters. Includes a useful index and bibliography. Summing Up: Recommended.

Upper-division undergraduates, graduate students, and professionals. (D. V. Chopra, Choice, Vol. 48 (8), April, 2011) This book describes the linear regression statistical models as a core of statistics, from simple linear regression (with one predictor variable) and analysis of variance (ANOVA) to more extended topics as multiple linear regression (with two or more predictor variables) and analysis of covariance (ANCOVA). … The contents of the book are addressed in most part to the undergraduates students (but with some chapters appropriate for master level) having a basic knowledge of linear algebra, probability and statistics. (Nicoleta Breaz, Zentralblatt MATH, Vol. 1245, 2012).