MP Applied Linear Regression Models-Revised Edition with Student CD / Edition 4

MP Applied Linear Regression Models-Revised Edition with Student CD / Edition 4

by Michael Kutner, Christopher Nachtsheim, John Neter, Christopher J. Nachtsheim
     
 

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ISBN-10: 0073014664

ISBN-13: 9780073014661

Pub. Date: 01/08/2004

Publisher: McGraw-Hill Higher Education

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

Overview

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.

Product Details

ISBN-13:
9780073014661
Publisher:
McGraw-Hill Higher Education
Publication date:
01/08/2004
Edition description:
List
Pages:
700
Sales rank:
567,777
Product dimensions:
7.70(w) x 9.30(h) x 1.30(d)

Table of Contents

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

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