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by Norman R. Draper and Harry Smith

Cover type: HardbackEdition: 3RD 98

Copyright: 1998

Publisher: John Wiley & Sons, Inc.

Published: 1998

International: No

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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.

**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.

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

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

Publisher Info

Publisher: John Wiley & Sons, Inc.

Published: 1998

International: No

Published: 1998

International: No

**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.

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