Kleinbaum D.Applied Regression Analysis and Other Multivariable Methods 5ed 2014
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Textbook in PDF format 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 Preview 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 Problems Introduction to Regression Analysis Preview Association versus Causality Statistical versus Deterministic Models Concluding Remarks 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 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 Problems 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? Problems 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 Example BRFSS Analysis Numerical Examples Problems Statistical Inference in Multiple Regression Preview 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 Problems Correlations Multiple, Partial, and Multiple Partial Preview 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 Problems Confounding and Interaction in Regression Preview Overview Interaction in Regression Confounding in Regression Summary and Conclusions Problems 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 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 Analysis of Covariance Several Nominal Independent Variables Comments and Cautions Problems Regression Diagnostics Preview 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 Problems 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 Selecting the Best Regression Equation Preview 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 Problems 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 Randomized Blocks Special Case of Two-Way ANOVA Preview 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 Problems 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 Two-Way ANOVA with Unequal Cell Numbers Preview 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 Problems The Method of Maximum Likelihood Preview The Principle of Maximum Likelihood Statistical Inference Using Maximum Likelihood Problems 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 Problems 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" Example Three Ordinal Categories and One Dichotomous Exposure Variable Ordinal Logistic Regression Example Using Real Data with Four Ordinal Categories and Three Predictor Variables Problems Poisson Regression Analysis Preview 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 Problems Analysis of Correlated Data Part The General Linear Mixed Model Preview Examples General Linear Mixed Model Approach Example Study of Effects of an Air Pollution Episode on FEV Levels Summary-Analysis of Correlated Data Part Problems Analysis of Correlated Data Part Random Effects and Other Issues Preview 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 Problems 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 Appendix A Tables Appendix B Matrices and Their Relationship to Regression Analysis Appendix C SAS Computer Appendix Appendix D Answers to Selected Problems