Presenting Statistical Results Effectively

Chapter 1: Some Foundation/What is a âModelâ?/Statistical Inference/Part A: General Principles of Effective Presentation/Chapter 2: Best Practices for Graphs and Tables/When to use Tables and Graphs/Constructing Effective Tables/Constructing Clear and Informative Graphs /Chapter 3: Methods for Visualizing Distributions/Displaying the Distributions of Categorical Variables/Displaying Distributions of Quantitative Variables/Transformations/Chapter 4: Exploring and Describing Relationships/Two Categorical Variables/Categorical Explanatory Variable and Quantitative Dependent Variable/Two quantitative Variables/Multivariate Displays/Part B: The Linear Model/Chapter 5: The Linear Regression Model/Ordinary Least Squares Regression/Hypothesis tests and confidence intervals/Assessing and Comparing Model Fit/Relative Importance of Predictors/Interpreting and presenting OLS models: Some empirical examples/Linear Probability Model/Chapter 6: Assessing the Impact and Importance of Multi-category Explanatory Variables/Coding Multi-category Explanatory Variables/Revisiting Statistical Significance: Multi-category Predictors/Relative importance of sets of regressors/Graphical Presentation of Additive Effects/Chapter 7: Identifying and Handling Problems in Linear Models/Nonlinearity/Influential Observations/Heteroskedasticity/Nonnormality/Chapter 8: Modelling and Presentation of Curvilinear Effects/Curvilinearity in the Linear Model Framework/Nonlinear Transformations/Polynomial Regression/Regression Splines/Nonparametric Regression/Generalized Additive Models/Chapter 9: Interaction Effects in Linear Models/Understanding Interaction Effects/Interactions Between Two Categorical Variables/Interactions Between One Categorical Variable and One Quantitative Variable/Interactions Between Two Continuous Variables/Interaction Effects: Some Cautions and Recommendations/Part C: The Generalized Linear Model and Extensions/Chapter 10: Generalized Linear Models/Basics of the Generalized Linear Model/Maximum Likelihood Estimation/Hypothesis tests and confidence intervals/Assessing Model Fit/Empirical Example: Using Poisson Regression to Predict Counts/Understanding Effects of Variables/Measuring Variable Importance/Model Diagnostics/Chapter 11: Categorical Dependent Variables/Regression Models for Binary Outcomes/Interpreting Effects in Logit and Probit Models/Model Fit for Binary Regression Models/Diagnostics Specific to Binary Regression Models/Extending the Binary Regression Model â" Ordered and Multinomial Models/Chapter 12: Conclusions and Recommendations/Choosing the Right Estimator/Research Design and Measurement Issues/Evaluating the Model/Effective Presentation of Results