Kleinbaum D.G., Kupper L.L., Nizam A., Rosenberg E.S. Applied Regression Analysis and Other Multivariable Methods

New York: Cengage Learning, 2013. — 1074 p.Concepts and Examples of Research.
Concepts.
Examples.
Concluding Remarks.Classification of Variables and the Choice of Analysis.
Classification of Variables.
Overlapping of Classification Schemes.
Choice of Analysis.Basic Statistics A Review.Descriptive Statistics.
Random Variables and Distributions.
Sampling Distributions of t, x, and F.
Statistical Inference Estimation.
Statistical Inference Hypothesis Testing.
Error Rates, Power, and Sample Size.Introduction to Regression Analysis.Association versus Causality.
Statistical versus Deterministic Models.
Concluding Remarks.Straight-Line Regression Analysis.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.
The measure of the Quality of the Straight-Line Fit and Estimate of o.
Inferences about the Slope and Intercept.
Interpretations of Tests for Slope and Intercept.
The Mean Value of Y at a Specified Value of X.
Prediction of a New Value of Y at X.
Assessing the Appropriateness of the Straight-Line Model.
Example BRFSS Analysis.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.
Example BRFSS Analysis.
How Large Should r Be in Practice?The Analysis-of-Variance Table.The ANOVA Table for Straight-Line Regression.Multiple Regression Analysis General Considerations.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.
Example BRFSS Analysis.
Numerical Examples.Statistical Inference in Multiple Regression.Test for Significant Overall Regression.
Partial F Test.
Multiple Partial F Test.
Strategies for Using Partial F Tests.
Additional Inference Methods for Multiple Regression.
Example BRFSS Analysis.Correlations Multiple, Partial, and Multiple Partial.Correlation Matrix.
Multiple Correlation Coefficient.
Relationship of RY|X, X,, XK to the Multivariate Normal Distribution.
Partial Correlation Coefficient.
Alternative Representation of the Regression Model.
Multiple Partial Correlation.
Concluding Remarks.Confounding and Interaction in Regression.OverviewInteraction in Regression.
Confounding in Regression.
Summary and Conclusions.Dummy Variables in Regression.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.Analysis of Covariance and Other Methods for Adjusting Continuous Data.Adjustment Problem.
Analysis of Covariance.
Assumption of Parallelism A Potential Drawback.
Analysis of Covariance Several Groups and Several Covariates.
Analysis of Covariance Several Nominal Independent Variables.
Comments and Cautions.Regression Diagnostics.Simple Approaches to Diagnosing Problems in Data.
Residual Analysis Detecting Outliers and Violations of Model Assumptions.
Strategies for Addressing Violations of Regression Assumptions.
Collinearity.
Diagnostics Example.Polynomial Regression.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.Selecting the Best Regression Equation.Steps in Selecting the Best Regression Equation Prediction Goal.
Step Specifying the Maximum Model Prediction Goal.
Step Specifying a Criterion for Selecting a Model Prediction Goal.
Step Specifying a Strategy for Selecting Variables Prediction Goal.
Step Conducting the Analysis Prediction Goal.
Step Evaluating Reliability with Split Samples Prediction Goal.
Example Analysis of Actual Data.
Selecting the Most Valid Model.One-Way Analysis of Variance.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.Randomized Blocks Special Case of Two-Way ANOVA.Equivalent Analysis of a Matched-Pairs Experiment.
Principle of Blocking.
Analysis of a Randomized-Blocks Study.
ANOVA Table for a Randomized-Blocks Study.
Regression Models for a Randomized-Blocks Study.
Fixed-Effects ANOVA Model for a Randomized-Blocks Study.Two-Way ANOVA with Equal Cell Numbers.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.Two-Way ANOVA with Unequal Cell Numbers.Presentation of Data for Two-Way ANOVA Unequal Cell Numbers.
Problem with Unequal Cell Numbers Nonorthogonality.
Regression Approafor Unequal Cell Sample Sizes.
Higher-Way ANOVA.The Method of Maximum Likelihood.The Principle of Maximum Likelihood.
Statistical Inference Using Maximum Likelihood.Logistic Regression Analysis.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.Polytomous and Ordinal Logistic Regression.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" Example Three Ordinal Categories and One Dichotomous Exposure Variable.
Ordinal Logistic Regression Example Using Real Data with Four Ordinal Categories and Three Predictor Variables.Poisson Regression Analysis.The Poisson Distribution.
An Example of Poisson Regression.
Poisson Regression.
Measures of Goodness of Fit.
Continuation of Skin Cancer Data Example.
A Second Illustration of Poisson Regression Analysis.Analysis of Correlated Data Part The General Linear Mixed Model.Examples.
General Linear Mixed Model Approach.
Example Study of Effects of an Air Pollution Episode on FEV Levels.
Summary-Analysis of Correlated Data Part.Analysis of Correlated Data Part Random Effects and Other Issues.Random Effects Revisited.
Results for Models with Random Effects Applied to Air Pollution Study Data.
Second Example-Analysis of Posture Measurement Data.
Recommendations about Choice of Correlation Structure.
Analysis of Data for Discrete Outcomes.Sample Size Planning for Linear and Logistic Regression and Analysis of Variance.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.Appendix A Tables.
Appendix B Matrices and Their Relationship to Regression Analysis.
Appendix C SAS Computer Appendix.
Appendix D Answers to Selected Problems.