Perfect for any statistics student or researcher, this book offers hands-on guidance on how to interpret and discuss your results in a way that not only gives them meaning, but also achieves maximum impact on your target audience. No matter what variables your data involves, it offers a roadmap for analysis and presentation that can be extended to other models and contexts.Focused on best practices for building statistical models and effectively communicating their results, this book helps you:        Find the right analytic and presentation techniques for your type of data        Understand the cognitive processes involved in decoding information        Assess distributions and relationships among variables        Know when and how to choose tables or graphs        Build, compare, and present results for linear and non-linear models        Work with univariate, bivariate, and multivariate distributions        Communicate the processes involved in and importance of your results.

Chapter 1: Some Foundation What is a 'Model'? Statistical InferencePart A: General Principles of Effective PresentationChapter 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 TransformationsChapter 4: Exploring and Describing Relationships Two Categorical Variables Categorical Explanatory Variable and Quantitative Dependent Variable Two quantitative Variables Multivariate DisplaysPart B: The Linear ModelChapter 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 ModelChapter 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 EffectsChapter 7: Identifying and Handling Problems in Linear Models Nonlinearity Influential Observations Heteroskedasticity NonnormalityChapter 8: Modelling and Presentation of Curvilinear Effects Curvilinearity in the Linear Model Framework Nonlinear Transformations Polynomial Regression Regression Splines Nonparametric Regression Generalized Additive ModelsChapter 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 RecommendationsPart C: The Generalized Linear Model and ExtensionsChapter 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 DiagnosticsChapter 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 - Ordered and Multinomial ModelsChapter 12: Conclusions and Recommendations Choosing the Right Estimator Research Design and Measurement Issues Evaluating the Model Effective Presentation of Results