Regression analysis and linear models : concepts, applications, and implementation / Richard B. Darlington, Andrew F. Hayes.

Format:

Book

Author/Creator:

Darlington, Richard B., author.

Hayes, Andrew F., author.

Series:

Methodology in the social sciences.

Methodology in the Social Sciences

Language:

English

Subjects (All):

Regression analysis.

Linear models (Statistics).

Psychology--Statistical methods.

Psychology.

Social sciences--Statistical methods.

Social sciences.

Physical Description:

1 online resource (661 pages)

Place of Publication:

New York, New York ; London, [England] : The Guilford Press, 2017.

Summary:

Emphasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPS

Contents:

Cover; Half Title Page; Series Page; Title Page; Copyright; Series Editor's Note; Preface; List of Symbols and Abbreviations; Contents; 1. Statistical Control and Linear Models; 1.1 Statistical Control; 1.1.1 The Need for Control; 1.1.2 Five Methods of Control; 1.1.3 Examples of Statistical Control; 1.2 An Overview of Linear Models; 1.2.1 What You Should Know Already; 1.2.2 Statistical Software for Linear Modeling and Statistical Control; 1.2.3 About Formulas; 1.2.4 On Symbolic Representations; 1.3 Chapter Summary; 2. The Simple Regression Model; 2.1 Scatterplots and Conditional Distributions

2.1.1 Scatterplots2.1.2 A Line through Conditional Means; 2.1.3 Errors of Estimate; 2.2 The Simple Regression Model; 2.2.1 The Regression Line; 2.2.2 Variance, Covariance, and Correlation; 2.2.3 Finding the Regression Line; 2.2.4 Example Computations; 2.2.5 Linear Regression Analysis by Computer; 2.3 The Regression Coefficient versus the Correlation Coefficient; 2.3.1 Properties of the Regression and Correlation Coefficients; 2.3.2 Uses of the Regression and Correlation Coefficients; 2.4 Residuals; 2.4.1 The Three Components of Y; 2.4.2 Algebraic Properties of Residuals

2.4.3 Residuals as Y Adjusted for Differences in X2.4.4 Residual Analysis; 2.5 Chapter Summary; 3. Partial Relationship and the Multiple Regression Model; 3.1 Regression Analysis with More Than One Predictor Variable; 3.1.1 An Example; 3.1.2 Regressors; 3.1.3 Models; 3.1.4 Representing a Model Geometrically; 3.1.5 Model Errors; 3.1.6 An Alternative View of the Model; 3.2 The Best-Fitting Model; 3.2.1 Model Estimation with Computer Software; 3.2.2 Partial Regression Coefficients; 3.2.3 The Regression Constant; 3.2.4 Problems with Three or More Regressors; 3.2.5 The Multiple Correlation R

3.3 Scale-Free Measures of Partial Association3.3.1 Semipartial Correlation; 3.3.2 Partial Correlation; 3.3.3 The Standardized Regression Coefficient; 3.4 Some Relations among Statistics; 3.4.1 Relations among Simple, Multiple, Partial, and Semipartial Correlations; 3.4.2 Venn Diagrams; 3.4.3 Partial Relationships and Simple Relationships May Have Different Signs; 3.4.4 How Covariates Affect Regression Coefficients; 3.4.5 Formulas for bj , prj , srj, and R; 3.5 Chapter Summary; 4. Statistical Inference in Regression; 4.1 Concepts in Statistical Inference; 4.1.1 Statistics and Parameters

4.1.2 Assumptions for Proper Inference4.1.3 Expected Values and Unbiased Estimation; 4.2 The ANOVA Summary Table; 4.2.1 Data = Model + Error; 4.2.2 Total and Regression Sums of Squares; 4.2.3 Degrees of Freedom; 4.2.4 Mean Squares; 4.3 Inference about the Multiple Correlation; 4.3.1 Biased and Less Biased Estimation of TR2; 4.3.2 Testing a Hypothesis about TR; 4.4 The Distribution of and Inference about a Partial Regression Coefficient; 4.4.1 Testing a Null Hypothesis about Tbj; 4.4.2 Interval Estimates for Tbj; 4.4.3 Factors Affecting the Standard Error of bj; 4.4.4 Tolerance

4.5 Inferences about Partial Correlations

Notes:

Description based upon print version of record.

Includes bibliographical references and indexes.

Description based on print version record.

ISBN:

1-4625-2798-1

1-4625-2799-X