Contents Page: Applying Regression and Correlation

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Preface6.1
 

Part I: I need to do regression analysis tomorrow

Chapter 1 Building Models with Regression and Correlation 
1.1 What are Models? 
1.2 Least Squares Models 
1.3 Modelling Relationships 
1.4 Looking More at Correlations 
1.5 Further Reading
Chapter 2 More Than One IV – Multiple Regression
2.1 Introduction: Multiple Regression in Theory
2.2 Part I: What’s It All About?
2.3 Multiple Regression in Practice
2.4 R and R Square
2.5 Adjusted R Square
2.6 Analysis of Variance (ANOVA) Table
2.7 Coefficients
2.8 Variable Entry
2.9 Methods of Variable Entry
2.10 Further Reading
Chapter 3 Categorical Independent Variables
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Part II: I need to do regression analysis next week

Chapter 4 Assumptions In Regression Analysis
4.1 Introduction
4.2 Assumptions About Measures
4.3 Assumptions About Data
4.4 Univariate Distribution Checks
4.5 Calculation Based Methods
4.6 Dealing with Outliers, Skew and Kurtosis
4.7 Multivariate Distributions
4.8 Further Reading
Chapter 5 Issues in Regression Analysis
5.1 Causality
5.2 Sample Size
5.3 Collinearity
5.4 Measurement Error
5.5 Further Reading

Part III: I would like to know more of the things that regression analysis can do

Chapter 6 Nonlinear and Logistic Regression
6.1
6.2 Nonlinear Regression
6.2  Logistic Regression
Chapter 7 Moderator And Mediator Analysis
7.1  Nonlinear Regression
7.1.1  Linear and curvilinear relationships
7.1.2  Generating a Curve
7.1.3  Carrying Out Nonlinear Regression
7.1.4 An Example of Non-Linear Regression
7.2 Logistic Regression
7.2.1  The Case of the Dichotomous Dependent Variable
7.2.2  The Logit Transformation
7.2.3  Using the Logit: Logistic Regression
7.2.4  An Annotated Example of Logistic Regression
7.2.5  Hierarchical Logistic Regression
7.2.6  Polynomial Logistic Regression
7.3 Further reading
Chapter 8 Introducing Some Advanced Techniques: Multilevel Modelling And Structural Equation Modelling.
8.1 Multilevel Modelling (MLM)
8.1.1  Why Use MLM
8.1.2  Algebraic Formulation
8.1.3  Hierarchies Everywhere
8.2 Structural Equation Modelling
8.2.1 Why use SEM
8.2.2  Identification
8.2.3 Latent Variables
8.2.4 Estimation in SEM
8.2.5 Model Testing
8.2.6 Some More Complex Models
8.3 Software for MLM and SEM
8.4 Further Reading
Appendix A Equations used in regression
Appendix B Using Computers to do regression
Appendix C Tables of Critical Values

 

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