Kutner, Nachtsheim, Neter, Wasserman, Applied Linear Regression Models, 4/e (ALRM4e) is the long established leading authoritative text and reference on regression (previously Neter was lead author.) For students in most any discipline where statistical analysis or interpretation is used, ALRM has served as the industry standard. The text includes brief introductory and review material, and then proceeds through regression and modeling. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Comments" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, and projects are drawn from virtually all disciplines and fields providing motivation for students in any discipline. ALRM 4e provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor.

Thoroughly updated and more straightforward than ever, Applied Linear Regression Models includes the latest statistics, developments, and methods in multicategory logistic regression; expanded treatment of diagnostics for logistic regression; a more powerful Levene test; and more. Cases, datasets, and examples allow for a more real-world perspective and explore relevant uses of regression techniques in business today.

Thoroughly updated and more straightforward than ever, Applied Linear Regression Models includes the latest statistics, developments, and methods in multicategory logistic regression; expanded treatment of diagnostics for logistic regression; a more powerful Levene test; and more. Cases, datasets, and examples allow for a more real-world perspective and explore relevant uses of regression techniques in business today.

Michael H. Kutner is a professor at Emory University in Atlanta.

Chris J. Nachtsheim is a professor at the University of Minnesota--Minneapolis.

John Neter is a professor at the University of Georgia in Athens.

Part1 Simple Linear Regression

1 Linear Regression with One Predictor Variable

2 Inferences in Regression and Correlation Analysis

3 Diagnostics and Remedial Measures

4 Simultaneous Inferences and Other Topics in Regression Analysis

5 Matrix Approach to Simple Linear Regression Analysis

Part 2 Multiple Linear Regression

6 Multiple Regression I

7 Multiple Regression II

8 Building the Regression Model I: Models for Quantitative and Qualitative Predictors

9 Building the Regression Model II: Model Selection and Validation

10 Building the Regression Model III: Diagnostics

11 Remedial Measures and Alternative Regression Techniques

12 Autocorrelation in Time Series Data

Part 3 Nonlinear Regression

13 Introduction to Nonlinear Regression and Neural Networks

14 Logistic Regression, Poisson Regression, and Generalized Linear Models