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Applied Regression Analysis and Other Multivariable Methods by Lawrence Kupper, Keith Muller, David Kleinbaum and Azhar Nizam (2007, Hardcover)

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Product Identifiers

PublisherBrooks/Cole

ISBN-100495384968

ISBN-139780495384960

eBay Product ID (ePID)57044825

Product Key Features

Number of Pages928 Pages

Publication NameApplied Regression Analysis and Other Multivariable Methods

LanguageEnglish

Publication Year2007

SubjectProbability & Statistics / Regression Analysis, Probability & Statistics / Multivariate Analysis, Probability & Statistics / General

TypeTextbook

AuthorLawrence Kupper, Keith Muller, David Kleinbaum, Azhar Nizam

Subject AreaMathematics

FormatHardcover

Dimensions

Item Height1.5 in

Item Weight53.7 Oz

Item Length9.3 in

Item Width7.4 in

Additional Product Features

Edition Number4

Intended AudienceCollege Audience

Dewey Edition23

IllustratedYes

Dewey Decimal519.5/3

Table Of Content1. 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, fÓ2, 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 fã2. Inferences About the Slope and Intercept. Interpretations of Tests for Slope and Intercept. Inferences About the Regression Line fÝY X = fÒ0 + fÒ1X . 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

SynopsisThis 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.