Applied Linear Regression Models / Edition 3

Applied Linear Regression Models / Edition 3

by John Neter, William Wasserman, Michael H. Kutner, Christopher J. Nachtsheim
     
 

ISBN-10: 025608601X

ISBN-13: 9780256086010

Pub. Date: 01/01/1996

Publisher: McGraw-Hill Higher Education

Applied Linear Regression Models was listed in the newsletter of the Decision Sciences Institute as a classic in its field and a text that should be on every member's shelf. The third edition continues this tradition. It is a successful blend of theory and application. The authors have taken an applied approach, and emphasize understanding concepts; this text…  See more details below

Overview

Applied Linear Regression Models was listed in the newsletter of the Decision Sciences Institute as a classic in its field and a text that should be on every member's shelf. The third edition continues this tradition. It is a successful blend of theory and application. The authors have taken an applied approach, and emphasize understanding concepts; this text demonstrates their approach trough worked-out examples. Sufficient theory is provided so that applications of regression analysis can be carried out with understanding. John Neter is past president of the Decision Science Institute, and Michael Kutner is a top statistician in the health and life sciences area. Applied Linear Regression Models should be sold into the one-term course that focuses on regression models and applications. This is likely to be required for undergraduate and graduate students majoring in allied health, business, economics, and life sciences.

Product Details

ISBN-13:
9780256086010
Publisher:
McGraw-Hill Higher Education
Publication date:
01/01/1996
Edition description:
Third Edition
Pages:
719
Product dimensions:
8.16(w) x 10.24(h) x 1.17(d)

Related Subjects

Table of Contents

1Linear Regression with One Independent Variable3
2Inferences in Regression Analysis44
3Diagnostics and Remedial Measures95
4Simultaneous Inferences and Other Topics in Regression Analysis152
5Matrix Approach to Simple Linear Regression Analysis176
6Multiple Regression - I217
7Multiple Regression - II260
8Building the Regression Model I: Selection of Predictor Variables327
9Building the Regression Model II: Diagnostics361
10Building the Regression Model III: Remedial Measures and Validation400
11Qualitative Predictor Variables455
12Autocorrelation in Time Series Data497
13Introduction to Nonlinear Regression531
14Logistic Regression, Poisson Regression, and Generalized Linear Models567
15Normal Correlation Models631
AppendixA Some Basic Results in Probability and Statistics663
Appendix B Tables685
Appendix C Data Sets702
Appendix D Selected Bibliography708
Index715

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