Rent 9780495384960 | Applied Regression Analysis and Other Multivariable Methods | Kleinbaum, David G.; Kupper, Lawrence L.; Nizam, Azhar; Muller, Keith E. @knetbooks.com

Summary Table of Contents

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.

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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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