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Applied Linear Regression Models - Text Only

Applied Linear Regression Models - Text Only - 3rd edition

Cover of Applied Linear Regression Models - Text Only 3RD 96 (ISBN -)
Edition: 3RD 96
Copyright: 1996
Publisher: Richard D. Irwin, Inc.
Published: 1996
International: No

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Applied Linear Regression Models - Text Only - 3RD 96 edition

ISBN13: -

John Neter, Michael H. Kutner, William Wasserman and Chris Nachtsheim

Edition: 3RD 96
Copyright: 1996
Publisher: Richard D. Irwin, Inc.
Published: 1996
International: No

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 ninth edition continues this tradition. It is a successful blend of theory and application. The authors have taken an applied approach. Their emphasis is on understanding concepts; they demonstrate by means of 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.

  • Multiple linear regression analysis discussion start the text.
  • Polynomial regression in now woven into the discussion of multiple linear regression.
  • Qualitative predictor variables now follows discussion of multiple regression model building and diagnostics.
  • There is an expanded discussion of diagnostics and remedial measures.
  • New topics added include: robust tests for constancy of the error variance, smoothing techniques to explore the shape of the regression function, robust regression and nonparametric regression techniques, bootstrapping methods for evaluating the precision of sample estimates for complex situations, and estimation of the variance and standard derivation functions to obtain weights for weighted least squares.
  • The Third Edition includes three chapters on model- building process for regression, including computer-assisted selection procedures.
  • Chapter 14 has been revised and expanded to include introduction to polytomous logistic regression, Poisson regression, and generalized linear models.

Table of Contents

1. Linear Regression with One Independent Variable

2. Inferences in Regression Analysis

3. Diagnostic and Remedial Measures, I

4. Simultaneous Inferences and Other Topics in Regression Analysis

5. Matrix Approach to Simple Linear Regression Analysis

6. Multiple Regression, I

7. Multiple Regression, II

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


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