Applied Linear Regression Models / Edition 2

Applied Linear Regression Models / Edition 2

by John Neter, William Wasserman, Michael H. Kutner
     
 

ISBN-10: 0256070687

ISBN-13: 9780256070682

Pub. Date: 12/28/1988

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

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:
9780256070682
Publisher:
McGraw-Hill Higher Education
Publication date:
12/28/1988
Edition description:
Older Edition
Pages:
688

Related Subjects

Table of Contents

1 Linear Regression with One Independent Variable2 Inferences in Regression Analysis 3 Diagnostic and Remedial Measures 4 Simultaneous Inferences and Other Topics in Regression Analysis 5 Matrix Approach to Simple Linear Regression Analysis 6 Multiple Regression 7 Multiple Regression 8 Building the Regression Model I: Selection of Predictor Variables 9 Building the Regression Model II: Diagnostics 10 Building the Regression Model III: Remedial Measures and Validation 11 Qualitative Predictor Variables 12 Autocorrelation in Time Series Data 13 Introduction to Nonlinear Regression 14 Logistic Regression, Poisson Regression, and Generalized Linear Models 15 Normal Correlation Models Appendixes

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