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

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Overview

Thoroughly updated and more straightforward than ever, Applied Linear Regression Models includes the latest statistics, developments, and methods in multicategory logistic regression; expanded treatment of diagnostics for logistic regression; a more powerful Levene test; and more. Cases, datasets, and examples allow for a more real-world perspective and explore relevant uses of regression techniques in business today.

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Product Details

  • ISBN-13: 9780073014661
  • Publisher: McGraw-Hill Higher Education
  • Publication date: 1/8/2004
  • Edition description: New Edition
  • Edition number: 4
  • Pages: 700
  • Sales rank: 461,202
  • Product dimensions: 7.70 (w) x 9.30 (h) x 1.30 (d)

Meet the Author

Michael H. Kutner is a professor at Emory University in Atlanta.

Chris J. Nachtsheim is a professor at the University of Minnesota—Minneapolis.

John Neter is a professor at the University of Georgia in Athens.

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Table of Contents

Pt. 1 Simple linear regression 1
Ch. 1 Linear regression with one predictor variable 2
Ch. 2 Inferences in regression and correlation analysis 40
Ch. 3 Diagnostics and remedial measures 100
Ch. 4 Simultaneous inferences and other topics in regression analysis 154
Ch. 5 Matrix approach to simple linear regression analysis 176
Pt. 2 Multiple linear regression 213
Ch. 6 Multiple regression I 214
Ch. 7 Multiple regression II 256
Ch. 8 Regression models for quantitative and qualitative predictors 294
Ch. 9 Building the regression model I : model selection and validation 343
Ch. 10 Building the regression model II : diagnostics 384
Ch. 11 Building the regression model III : remedial measures 421
Ch. 12 Autocorrelation in time series data 481
Pt. 3 Nonlinear regression 509
Ch. 13 Introduction to nonlinear regression and neural networks 510
Ch. 14 Logistic regression, Poisson regression, and generalized linear models 555
App. A Some basic results in probability and statistics 641
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