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Applied Regression Analysis and Other Multivariable Methods 5th Edition

David G. Kleinbaum, Lawrence L. Kupper, Azhar Nizam, Eli S. Rosenberg

  • Published
  • Previous Editions 2008, 1998, 1988
  • 1072 Pages

Overview

This bestseller is known for its modern, practical approach to regression-analysis methods that students will find applicable to real-life problems. APPLIED REGRESSION ANALYSIS AND MULTIVARIABLE METHODS highlights the role of the computer in contemporary statistics with numerous printouts and exercises that can be solved with 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. Available with InfoTrac® Student Collections http://gocengage.com/infotrac.

David G. Kleinbaum, Emory University

Lawrence L. Kupper, University of North Carolina at Chapel Hill

Azhar Nizam, Emory University

Eli S. Rosenberg, Emory University

  • New Linear Regression Examples throughout the text.
  • New SAS Computer Appendix.
  • InfoTrac® Student Collections are specialized databases expertly drawn from the Gale Academic One library. Each InfoTrac® Student Collection enhances the student learning experience in the specific course area related to the product. These specialized databases allow access to hundreds of scholarly and popular publications - all reliable sources - including journals, encyclopedias, and academic reports. Learn more and access at: http://gocengage.com/infotrac.
  • New and Revised Problems in selected chapters.
  • Revised, clarified exposition and a Revised Sample Size Chapter.
  • Improved Design for maximum student engagement.
  • Numerous examples and exercises illustrate applications to real studies in a wide variety of disciplines.
  • Numerous examples and exercises illustrate applications to real studies in a wide variety of disciplines.
  • Several advanced topics are presented in a unique, nonmathematical manner.
  • The quantity of computer results throughout the text has been greatly expanded. Computer output has been used to replace material that unnecessarily emphasized numerical calculations.
  • The complete set of data for most exercises is provided, along with related computer results. This allows the instructor to assign computer work based on available packaged programs.
  • Numerous examples and exercises illustrate applications to real studies in a wide variety of disciplines.
  • Chapters on Polytomous and Ordinal Logistic Regression (Chapter 23) and Sample Size Determination (Chapter 27).
  • Chapters on maximum likelihood (ML) estimation (Chapter 21), and analysis of correlated data (Chapter 25, 26).
  • Updated content, with new exercises added to several chapters.
1. CONCEPTS AND EXAMPLES OF RESEARCH.
Concepts. Examples. Concluding Remarks. References.
2. CLASSIFICATION OF VARIABLES AND THE CHOICE OF ANALYSIS.
Classification of Variables. Overlapping of Classification Schemes. Choice of Analysis. References.
3. BASIC STATISTICS: A REVIEW.
Preview. Descriptive Statistics. Random Variables and Distributions. Sampling Distributions of t, �Ó2, and F. Statistical Inference: Estimation. Statistical Inference: Hypothesis Testing. Error Rate, Power, and Sample Size. Problems. References.
4. INTRODUCTION TO REGRESSION ANALYSIS.
Preview. Association versus Causality. Statistical versus Deterministic Models. Concluding Remarks. References.
5. 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 �ã2. Inferences About the Slope and Intercept. Interpretations of Tests for Slope and Intercept. Inferences About the Regression Line �ÝY|X = �Ò0 + �Ò1X . Prediction of a New Value of Y at X0. Problems. References.
6. 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.
7. THE ANALYSIS-OF-VARIANCE TABLE.
Preview. The ANOVA Table for Straight-line Regression. Problems.
8. 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.
9. 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.
10. CORRELATIONS: MULTIPLE, PARTIAL, AND MULTIPLE PARTIAL.
Preview. Correlation Matrix. Multiple Correlation Coefficient. Relationship of RY|X1, X2, ¡KXk to the Multivariate Normal Distribution. Partial Correlation Coefficient. Alternative Representation of the Regression Model. Multiple Partial Correlation. Concluding Remarks. Problems. References.
11. CONFOUNDING AND INTERACTION IN REGRESSION.
Preview. Overview. Interaction in Regression. Confounding in Regression. Summary and Conclusions. Problems. References.
12. 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.
13. 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.
14. 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.
15. POLYNOMIAL REGRESSION.
Preview. Polynomial Models. Least-squares Procedure for Fitting a Parabola. ANOVA Table for Second-order Polynomial Regression. Inferences Associated with Second-order Polynomial Regression. Example Requiring a Second-order Model. Fitting and Testing Higher-order Model. Lack-of-fit Tests. Orthogonal Polynomials. Strategies for Choosing a Polynomial Model. Problems.
16. SELECTING THE BEST REGRESSION EQUATION.
Preview. Steps in Selecting the Best Regression Equation. Step 1: Specifying the Maximum Model. Step 2: Specifying a Criterion for Selecting a Model. Step 3: Specifying a Strategy for Selecting Variables. Step 4: Conducting the Analysis. Step 5: Evaluating Reliability with Split Samples. Example Analysis of Actual Data. Issues in Selecting the Most Valid Model. Problems. References.
17. ONE-WAY ANALYSIS OF VARIANCE.
Preview. One-way ANOVA: The Problem, Assumptions, and Data Configuration. for One-way Fixed-effects ANOVA. Regression Model for Fixed-effects One-way ANOVA Fixed-effects Model for One-way ANOVA. Random-effects Model for One-way ANOVA. -comparison Procedures for Fixed-effects One-way ANOVA. a Multiple-comparison Technique. Orthogonal Contrasts and Partitioning an ANOVA Sum of Squares. Problems. References.
18. RANDOMIZED BLOCKS: SPECIAL CASE OF TWO-WAY ANOVA.
Preview. Equivalent Analysis of a Matched-pairs Experiment. Principle of Blocking.
Analysis of a Randomized-blocks Experiment. ANOVA Table for a Randomized-blocks
Experiment. Models for a Randomized-blocks Experiment. Fixed-effects ANOVA Model for a Randomized-blocks Experiment. Problems. References.
19. TWO-WAY ANOVA WITH EQUAL CELL NUMBERS.
Preview. Using a Table of Cell Means. General Methodology. F Tests for Two-way ANOVA. Regression Model for Fixed-effects Two-way ANOVA. Interactions in Two-way ANOVA. Random- and Mixed-effects Two-way ANOVA Models. Problems. References.
20. TWO-WAY ANOVA WITH UNEQUAL CELL NUMBERS.
Preview. Problem with Unequal Cell Numbers: Nonorthogonality. Regression Approach for Unequal Cell Sample Sizes. Higher-way ANOVA. Problems. References.
21. THE METHOD OF MAXIMUM LIKELIHOOD.
Preview. The Principle of Maximum Likelihood. Statistical Inference Using Maximum Likelihood. Summary. Problems.
22. LOGISTIC REGRESSION ANALYSIS.
Preview. The Logistic Model. Estimating the Odds Ratio Using Logistic Regression. A Numerical Example of Logistic Regression. Theoretical Considerations. An Example of Conditional ML Estimation Involving Pair-matched Data with Unmatched Covariates. Summary. Problems. References.
23. POLYTOMOUS AND ORDINAL LOGISTIC REGRESSION.
Preview. Why Not Use Binary Regression? An Example of Polytomous Logistic Regression: One Predictor, Three Outcome Categories. An Example: Extending the Polytomous Logistic Model to Several Predictors. Ordinal Logistic Regression: Overview. A "Simple" Hypothetical Example: Three Ordinal Categories and One Dichotomous Exposure Variable. Ordinal Logistic Regression Example Using Real Data with Four Ordinal Categories and Three Predictor Variables. Summary. Problems. References.
24. POISSON REGRESSION ANALYSIS.
Preview. The Poisson Distribution. Example of Poisson Regression. Poisson Regression: General Considerations. Measures of Goodness of Fit. Continuation of Skin Cancer Data Example. A Second Illustration of Poisson Regression Analysis. Summary. Problems. References.
25. ANALYSIS OF CORRELATED DATA PART 1: THE GENERAL LINEAR MIXED MODEL.
Preview. Examples. General Linear Mixed Model Approach. Example: Study of Effects of an Air Polluion Episode on FEV1 Levels. Summary¡XAnalysis of Correlated Data: Part 1. Problems. References.
26. ANALYSIS OF CORRELATED DATA PART 2: RANDOM EFFECTS AND OTHER ISSUES.
Preview. Random Effects Revisited. Results for Random Effects Models Applied to Air Pollution Study Data. Second Example¡XAnalysis of Posture Measurement Data. Recommendations about Choice of Correlation Structure. Analysis of Data for Discrete Outcomes. Problems. References.
27. SAMPLE SIZE PLANNING FOR LINEAR AND LOGISTIC REGRESSION AND ANALYSIS OF VARIANCE.
Preview. Review: Sample Size Calculations for Comparisons of Means and Proportions. Sample Size Planning for Linear Regression. Sample Size Planning for Logistic
Regression. Power and Sample Size Determination for Linear Models: A General Approach. Sample Size Determination for Matched Case-control Studies with a Dichotomous Outcome. Practical Considerations and Cautions. Problems. References.
Appendix A.
Appendix B.
Appendix C.
Solutions to Exercises.
Index.

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This printed manual contains complete worked solutions to Applied Regression Analysis and Other Multivariable Methods, 5e.

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ISBN: 9781285175072
The SSM features worked solutions to select problems in Applied Regression Analysis and Other Multivariable Methods, 5e.

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Student Solutions Manual

ISBN: 9781285175072
The SSM features worked solutions to select problems in Applied Regression Analysis and Other Multivariable Methods, 5.