Presenting statistical results effectively

Table of content:
Chapter 1: Some Foundation
What is a ‘Model’?

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 – 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
[https://us.sagepub.com/en-us/nam/presenting-statistical-results-effectively/book240604#contents]

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.

(https://us.sagepub.com/en-us/nam/presenting-statistical-results-effectively/book240604)