# Applied Linear Regression Models / Edition 3

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

 1 Linear Regression with One Independent Variable 3 2 Inferences in Regression Analysis 44 3 Diagnostics and Remedial Measures 95 4 Simultaneous Inferences and Other Topics in Regression Analysis 152 5 Matrix Approach to Simple Linear Regression Analysis 176 6 Multiple Regression - I 217 7 Multiple Regression - II 260 8 Building the Regression Model I: Selection of Predictor Variables 327 9 Building the Regression Model II: Diagnostics 361 10 Building the Regression Model III: Remedial Measures and Validation 400 11 Qualitative Predictor Variables 455 12 Autocorrelation in Time Series Data 497 13 Introduction to Nonlinear Regression 531 14 Logistic Regression, Poisson Regression, and Generalized Linear Models 567 15 Normal Correlation Models 631 Appendix A Some Basic Results in Probability and Statistics 663 Appendix B Tables 685 Appendix C Data Sets 702 Appendix D Selected Bibliography 708 Index 715