Applied Linear Regression Models by Kutner, Nachsteim and Neter (4th edition) or, Applied Linear Statistical Models by

Kutner, Nachtsteim, Neter and Li (5th edition). Both published by McGraw-Hill/Irwin. NOTE on the book(s). The rst 14

chapters of Applied Linear Statistical Models (ALSM) are EXACTLY equivalent to the 14 chapters that make up Applied

Linear Regression Models, 4th ed., with the same pagination. The second half of ALSM covers experimental design and the

analysis of variance, and is used in our STAT 526. If you are going to take STAT 526, you should buy the Applied Linear

Statistical Models (but it is a large book).

Regression is the most widely used statistical technique. In addition to learning about regression methods this course will also reinforce basic statistical concepts and introduce students to "statistical thinking" in a broader context. This is primarily an applied statistics course. While models and methods are written out carefully with some basic derivations, the primary focus of the course is on the understanding and presentation of regression models and associated methods, data analysis, interpretation of results, statistical computation and model building. Topics covered include simple and multiple linear regression; correlation; the use of dummy variables; residuals and diagnostics; model building/variable selection, regression models and methods in matrix form. With time permitting, further topics include an introduction to weighted least squares, regression with correlated errors and nonlinear (including binary) regression.