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Applied Regression Analysis / With 3.5" Disk

Applied Regression Analysis / With 3.5" Disk - 3rd edition

ISBN13: 978-0471170822

Cover of Applied Regression Analysis / With 3.5" Disk 3RD 98 (ISBN 978-0471170822)
ISBN13: 978-0471170822
ISBN10: 0471170828
Cover type:
Edition/Copyright: 3RD 98
Publisher: John Wiley & Sons, Inc.
Published: 1998
International: No
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Applied Regression Analysis / With 3.5" Disk - 3RD 98 edition

ISBN13: 978-0471170822

Norman R. Draper and Harry Smith

ISBN13: 978-0471170822
ISBN10: 0471170828
Cover type:
Edition/Copyright: 3RD 98
Publisher: John Wiley & Sons, Inc.

Published: 1998
International: No
Summary

A major goal of scientific exploration is the discovery of relationships among variables. Regression is the analysis or measure of the relationship between a dependent variable and one or more independent variables. This text covers a commonly used statistical tool in constructing mathematical models from experimental data.

Author Bio

Draper, Norman R. : University of Wisconsin

Norman R. Draper teaches in the Department of Statistics at the University of Wisconsin.


Smith, Harry :

Harry Smith is a former faculty member of the Mt. Sinai School of Medicine.

Table of Contents

Basic Prerequisite Knowledge.

Fitting a Straight Line by Least Squares.

Checking the Straight Line Fit.

Fitting Straight Lines : Special Topics.

Regression in Matrix Terms : Straight Line Case.

The General Regression Situation.

Extra Sums of Squares and Tests for Several Parameters Being Zero.

Serial Correlation in the Residuals and the Durbin -- Watson Test.

More of Checking Fitted Models.

Multiple Regression : Special Topics.

Bias in Regression Estimates, and Expected Values of Mean Squares and Sums of Squares.

On Worthwhile Regressions, Big F's, and R2.

Models Containing Functions of the Predictors, Including Polynomial Models.

Transformation of the Response Variable.

"Dummy" Variables.

Selecting the "Best" Regression Equation.

Ill - Conditioning in Regression Data.

Ridge Regression.

Generalized Linear Models (GLIM).

Mixture Ingredients as Predictor Variables.

The Geometry of Least Squares.

More Geometry of Least Squares.

Orthogonal Polynomials and Summary Data.

Multiple Regression Applied to Analysis of Variance Problems.

An Introduction to Nonlinear Estimation.

Robust Regression.

Resampling Procedures (Bootstrapping).

Bibliography
True/False Questions
Answers to Exercises
Tables
Indexes

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