MP Applied Linear Regression Models-Revised Edition with Student CD by Michael H KutnerMP Applied Linear Regression Models-Revised Edition with Student CD by Michael H Kutner

MP Applied Linear Regression Models-Revised Edition with Student CD

byMichael H Kutner, Christopher J. Nachtsheim, John Neter

Hardcover | January 8, 2004

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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.
Title:MP Applied Linear Regression Models-Revised Edition with Student CDFormat:HardcoverDimensions:750 pages, 9.4 × 7.7 × 1.4 inPublished:January 8, 2004Publisher:McGraw-Hill EducationLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0073014664

ISBN - 13:9780073014661

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Table of Contents

Part1 Simple Linear Regression

1Linear Regression with One Predictor Variable

2Inferences in Regression and Correlation Analysis

3Diagnostics and Remedial Measures

4 Simultaneous Inferences and Other Topics in Regression Analysis

5Matrix Approach to Simple Linear Regression Analysis

Part 2Multiple Linear Regression

6Multiple Regression I

7 Multiple Regression II

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

9 Building the Regression Model II: Model Selection and Validation

10Building the Regression Model III: Diagnostics

11Remedial Measures and Alternative Regression Techniques

12Autocorrelation in Time Series Data

Part 3Nonlinear Regression

13Introduction to Nonlinear Regression and Neural Networks

14Logistic Regression, Poisson Regression, and Generalized Linear Models