Applied Regression Analysis and Other Multivariable Methods

by Kleinbaum, David G.; Kupper, Lawrence L.; Nizam, Azhar; Muller, Keith E.

• ISBN13: 9780495384960
• ISBN10: 0495384968
• Edition: 4th
• Format: Hardcover
• Copyright: 2007-04-23
• Publisher: Duxbury Press

Summary

This bestseller will help you learn regression-analysis methods that you can apply to real-life problems. It highlights the role of the computer in contemporary statistics with numerous printouts and exercises that you can solve using the computer. The authors continue to emphasize model development, the intuitive logic and assumptions that underlie the techniques covered, the purposes, advantages, and disadvantages of the techniques, and valid interpretations of those techniques.

Table of Contents

Concepts and Examples of Research

Concepts

Examples

Concluding Remarks

References

Classification of Variables and the Choice of Analysis

Classification of Variables

Overlapping of Classification Schemes

Choice of Analysis

References

Basic Statistics: A Review

Preview

Descriptive Statistics

Random Variables and Distributions

Sampling Distributions of t, ?+2, and F

Statistical Inference: Estimation

Statistical Inference: Hypothesis Testing

Error Rate, Power, and Sample Size

Problems

References

Introduction to Regression Analysis

Preview

Association versus Causality

Statistical versus Deterministic Models

Concluding Remarks

References

Straight-Line Regression Analysis

Preview

Regression with a Single Independent Variable

Mathematical Properties of a Straight Line

Statistical Assumptions for a Straight-line Model

Determining the Best-fitting Straight Line

Measure of the Quality of the Straight-line Fit and Estimate?

Inferences About the Slope and Intercept

Interpretations of Tests for Slope and Intercept

Inferences About the Regression Line ?&Y X = ?-0 + ?-1X

Prediction of a New Value of Y at X

Problems

References

The Correlation Coefficient and Straight-Line Regression Analysis

Definition of r

r as a Measure of Association

The Bivariate Normal Distribution

r and the Strength of the Straight-line Relationship

What r Does Not Measure

Tests of Hypotheses and Confidence Intervals for the Correlation Coefficient

Testing for the Equality of Two Correlations

Problems

References

The Analysis-of-Variance Table

Preview

The ANOVA Table for Straight-line Regression

Problems

Multiple Regression Analysis: General Considerations

Preview

Multiple Regression Models

Graphical Look at the Problem

Assumptions of Multiple Regression

Determining the Best Estimate of the Multiple Regression Equation

The ANOVA Table for Multiple Regression

Numerical Examples

Problems

References

Testing Hypotheses in Multiple Regression

Preview

Test for Significant Overall Regression

Partial F Test

Multiple Partial F Test

Strategies for Using Partial F Tests

Tests Involving the Intercept

Problems

References

Correlations: Multiple, Partial, and Multiple Partial

Preview

Correlation Matrix

Multiple Correlation Coefficient

Relationship of RY X1, X2, mKXk to the Multivariate Normal Distribution

Partial Correlation Coefficient

Alternative Representation of the Regression Model

Multiple Partial Correlation

Concluding Remarks

Problems

References

Confounding and Interaction in Regression

Preview

Overview

Interaction in Regression

Confounding in Regression

Summary and Conclusions

Problems

References

Dummy Variables in Regression

Preview

Definitions

Rule for Defining Dummy Variables

Comparing Two Straight-line Regression Equations: An Example

Questions for Comparing Two Straight Lines

Methods of Comparing Two Straight Lines

Method I: Using Separate Regression Fits to Compare Two Straight Lines

Method II: Using a Single Regression Equation to Compare Two Straight Lines

Comparison of Methods I and II

Testing Strategies and Interpretation: Comparing Two Straight Lines

Other Dummy Variable Models

Comparing Four Regression Equations

Comparing Several Regression Equations Involving Two Nominal Variables

Problems

References

Analysis of Covariance and Other Methods for Adjusting Continuous Data

Preview

Adjustment Problem

Analysis of Covariance

Assumption of Parallelism: A Potential Drawback

Analysis of Covariance: Several Groups and Several Covariates

Comments and Cautions

Summary Problems

Reference

Regression Diagnostics

Preview

Simple Approaches to Diagnosing Problems in Data

Residual Analysis: Detecting Outliers and Violations of Model Assumptions

Strategies of Analysis

Collinearity

Scaling Problems

Diagnostics Example

An Important Caution

Problems

References

Polynomial Regression

Preview

Polynomial

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