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Applied Linear Regression Models (Revised) With CD

Applied Linear Regression Models (Revised) With CD - 4th edition

ISBN13: 978-0073014661

Cover of Applied Linear Regression Models (Revised) With CD 4TH 04 (ISBN 978-0073014661)
ISBN13: 978-0073014661
ISBN10: 0073014664
Cover type: Hardback
Edition/Copyright: 4TH 04
Publisher: McGraw-Hill Publishing Company
Published: 2004
International: No
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Applied Linear Regression Models (Revised) With CD - 4TH 04 edition

ISBN13: 978-0073014661

Michael A. Kutner, Chris Nachtsheim and John Neter

ISBN13: 978-0073014661
ISBN10: 0073014664
Cover type: Hardback
Edition/Copyright: 4TH 04
Publisher: McGraw-Hill Publishing Company

Published: 2004
International: No
Summary

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.

New to This Edition

  • Thoroughly Updated to include the latest developments and methods in statistics such as: multi-category (polytomous) logistic regression for nominal and ordinal data, expanded treatment of diagnostics for logistic regression, Akaike's Information Criterion and Schwarz's Bayesian Criterion for model selection, a more powerful Levene test, advanced bootstrapping, inclusion of data mining techniques such as neural networks and regression trees, and more, these updates assure that students are efficient at the most current statistical tools available.
  • Improved Organization by combining related chapters and improving transitions between concepts this revision reflects a more useful organization making it easier for instructors to teach from and students to learn.
  • Cases, Datasets and Examples Improved cases have been added to assignment material, with examples from finance, data sets have been updated and now include larger data sets. These changes provide an effective "business" perspective exposing students to relevant uses of regression techniques in business today.
  • Enhanced integration of computing and automated methods now reflects a more current approach to implementing these techniques. This integration is included without sacrificing statistical literacy.

Features :

  • Thorough, comprehensive coverage has come to be known as "the bible of statistics." Provides students with the most current and authoritative coverage available.
  • Straightforward writing style, notation, and format for students in various disciplines, better preparing students for a wide range of jobs.
  • Clean and reliable for teaching and handy reference students can use in their careers.

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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