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 first 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 Stat 526.

If you are going to take Stat 526, you should buy the Applied Linear Statistical Models.

Regression analysis is the most popularly used statistical technique with application in almost every imaginable field. The focus of this course is on a careful understanding and of regression models and associated methods of statistical inference, 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; expressing regression models and methods in matrix form; an introduction to weighted least squares, regression with correlated errors and nonlinear regression. Extensive data analysis using SAS (no previous computer experience assumed). Requires prior coursework in Statistics, preferably Stat 516, and basic matrix algebra. Satisfies the Integrative Experience requirement for BA-Math and BS-Math majors.

## Department of Mathematics and Statistics