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Applied Regression Analysis and Multivariable Methods David G. Kleinbaum

Applied Regression Analysis and Multivariable Methods By David G. Kleinbaum

Applied Regression Analysis and Multivariable Methods by David G. Kleinbaum


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Summary

A practical and mathematically simple introduction to regression methods, applicable to real-life problems. The text emphasizes the intuitive logic and assumptions that underlie the techniques covered, their purposes, advantages and disadvantages, and valid interpretations.

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Applied Regression Analysis and Multivariable Methods Summary

Applied Regression Analysis and Multivariable Methods by David G. Kleinbaum

Regression students will appreciate this best-seller's modern, practical approach and its use of real-life problems. The third edition offers a significant shift in emphasis from calculation to interpretation, by replacing computational formulas with computer printouts in SAS (R). It highlights the role of the computer in contemporary statistics with numerous printouts and exercises that can be solved with the computer. Appropriate for statistics, biostatistics, mathematics, psychology, sociology, business, and industrial engineering students or anyone who intends to use regression analysis in their work, this text offers a traditional structure with a modern flavor. The authors emphasize model development; the intuitive logic and assumptions that underlie the techniques covered as well as the purposes, advantages and disadvantages, and valid interpretations of those techniques.

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Table of Contents

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, X(2), and F / Statistical Inference: Estimation / Statistical Inference: Hypothesis Testing / Error Rates, 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 of o2 / Inferences About the Slope and Intercept / Interpretations of Tests for Slope and Intercept / Inferences About the Regression Line Y|X = B0 = B1X / Prediction of a New Value of Y at X0 / Assessing the Appropriateness of the Straight-Line Model / 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,...Xk to the Multivariate Normal Distribution / Partial Correlation Coefficient / Alternative Representation of the Regression Model / Multiple Partial Correlation / Problems / Reference 11. CONFOUNDING AND INTERACTION IN REGRESSION Preview / Overview / Interaction in Regression / Confounding in Regression / Summary and Conclusions / Problems / References 12. REGRESSION DIAGNOSTICS Preview / Simple Approaches to Diagnosing Problems in Data / Residual Analysis / Treating Outliers / Collinearity / Scaling Problems / Treating Collinearity and Scaling Problems / Alternate Strategies of Analysis / An Important Caution / Problems / References 13. 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 Models / Lack-of-Fit Tests / Orthogonal Polynomials / Strategies for Choosing a Polynomial Model / Problems 14. 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 15. 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 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 / Methodology 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 / Multiple-Comparison Procedures for Fixed-Effects One-Way ANOVA / Choosing 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 Randomized-Blocks Experiment / ANOVA Table for a Randomized-Blocks Experiment / Regression 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 / Problems with Unequal Cell Numbers: Nonorthogonality / Regression Approach for Unequal Cell Sample Sizes / Higher-Way ANOVA / Problems / References 21. ANALYSIS OF REPEATED MEASURES DATA Preview / Examples / General Approach for Repeated Measures ANOVA / Overview of Selected Repeated Measures of Designs and ANOVA-Based Analyses / Repeated Measures ANOVA for Unbalanced Data / Other Approaches to Analyzing Repeated Measures Data / Appendix 12-A; Examples of SAS''''s GLM and MIXED Procedures / Problems / References 22. THE METHOD OF MAXIMUM LIKELIHOOD Preview / The Principle of Maximum Likelihood / Statistical Inference via Maximum Likelihood / Summary / Problems / References 23. 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 24. POISSON REGRESSION ANALYSIS Preview / The Poisson Distribution / An 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 / APPENDIX A: TABLES / APPENDIX B: MATRICES AND THEIR RELATIONSHIP TO REGRESSION ANALYSIS / APPENDIX C: SOLUTIONS TO EXERCISES / INDEX

Additional information

CIN0534209106VG
9780534209100
0534209106
Applied Regression Analysis and Multivariable Methods by David G. Kleinbaum
Used - Very Good
Hardback
Cengage Learning, Inc
1997-09-15
736
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
This is a used book - there is no escaping the fact it has been read by someone else and it will show signs of wear and previous use. Overall we expect it to be in very good condition, but if you are not entirely satisfied please get in touch with us

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