TY  - BOOK
AU  - Anderson, Robert
AU  - Armstrong II, David A
TI  - Presenting statistical results effectively
SN  - 9781446269817
U1  - 001.4226 
PY  - 2022///
CY  - London
PB  - Sage Publication Ltd.
KW  -  Research methods and evaluation
KW  -  Statistics - Graphic methods
KW  -  Statistical research
N1  - 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]
N2  - 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)
ER  -