Anderson, Robert <br/><br/>
Presenting statistical results effectively 

 - London  Sage Publication Ltd.  2022 
 - xxvi, 424 p. 
<br/><br/> Table of content:<br/>Chapter 1: Some Foundation <br/>What is a ‘Model’?<br/> <br/>Statistical Inference<br/> <br/> <br/>Part A: General Principles of Effective Presentation<br/>  Chapter 2: Best Practices for Graphs and Tables <br/>When to use Tables and Graphs<br/> <br/>Constructing Effective Tables<br/> <br/>Constructing Clear and Informative Graphs<br/> <br/>  Chapter 3: Methods for Visualizing Distributions <br/>Displaying the Distributions of Categorical Variables<br/> <br/>Displaying Distributions of Quantitative Variables<br/> <br/>Transformations<br/> <br/>  Chapter 4: Exploring and Describing Relationships <br/>Two Categorical Variables<br/> <br/>Categorical Explanatory Variable and Quantitative Dependent Variable<br/> <br/>Two quantitative Variables<br/> <br/>Multivariate Displays<br/> <br/> <br/>Part B: The Linear Model<br/>  Chapter 5: The Linear Regression Model <br/>Ordinary Least Squares Regression<br/> <br/>Hypothesis tests and confidence intervals<br/> <br/>Assessing and Comparing Model Fit<br/> <br/>Relative Importance of Predictors<br/> <br/>Interpreting and presenting OLS models: Some empirical examples<br/> <br/>Linear Probability Model<br/> <br/>  Chapter 6: Assessing the Impact and Importance of Multi-category Explanatory Variables <br/>Coding Multi-category Explanatory Variables<br/> <br/>Revisiting Statistical Significance: Multi-category Predictors<br/> <br/>Relative importance of sets of regressors<br/> <br/>Graphical Presentation of Additive Effects<br/> <br/>  Chapter 7: Identifying and Handling Problems in Linear Models <br/>Nonlinearity<br/> <br/>Influential Observations<br/> <br/>Heteroskedasticity<br/> <br/>Nonnormality<br/> <br/>  Chapter 8: Modelling and Presentation of Curvilinear Effects <br/>Curvilinearity in the Linear Model Framework<br/> <br/>Nonlinear Transformations<br/> <br/>Polynomial Regression<br/> <br/>Regression Splines<br/> <br/>Nonparametric Regression<br/> <br/>Generalized Additive Models<br/> <br/>  Chapter 9: Interaction Effects in Linear Models <br/>Understanding Interaction Effects<br/> <br/>Interactions Between Two Categorical Variables<br/> <br/>Interactions Between One Categorical Variable and One Quantitative Variable<br/> <br/>Interactions Between Two Continuous Variables<br/> <br/>Interaction Effects: Some Cautions and Recommendations<br/> <br/> <br/>Part C: The Generalized Linear Model and Extensions<br/>  Chapter 10: Generalized Linear Models <br/>Basics of the Generalized Linear Model<br/> <br/>Maximum Likelihood Estimation<br/> <br/>Hypothesis tests and confidence intervals<br/> <br/>Assessing Model Fit<br/> <br/>Empirical Example: Using Poisson Regression to Predict Counts<br/> <br/>Understanding Effects of Variables<br/> <br/>Measuring Variable Importance<br/> <br/>Model Diagnostics<br/> <br/>  Chapter 11: Categorical Dependent Variables <br/>Regression Models for Binary Outcomes<br/> <br/>Interpreting Effects in Logit and Probit Models<br/> <br/>Model Fit for Binary Regression Models<br/> <br/>Diagnostics Specific to Binary Regression Models<br/> <br/>Extending the Binary Regression Model – Ordered and Multinomial Models<br/> <br/>  Chapter 12: Conclusions and Recommendations <br/>Choosing the Right Estimator<br/> <br/>Research Design and Measurement Issues<br/> <br/>Evaluating the Model<br/> <br/>Effective Presentation of Results<br/>[https://us.sagepub.com/en-us/nam/presenting-statistical-results-effectively/book240604#contents] 
<br/><br/> 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.<br/><br/>Focused on best practices for building statistical models and effectively communicating their results, this book helps you:<br/>-        Find the right analytic and presentation techniques for your type of data<br/>-        Understand the cognitive processes involved in decoding information<br/>-        Assess distributions and relationships among variables<br/>-        Know when and how to choose tables or graphs<br/>-        Build, compare, and present results for linear and non-linear models<br/>-        Work with univariate, bivariate, and multivariate distributions<br/>-        Communicate the processes involved in and importance of your results. <br/><br/>(https://us.sagepub.com/en-us/nam/presenting-statistical-results-effectively/book240604) 
<br/><br/><label>ISBN: </label>9781446269817 
<br/><br/><label>Subjects--Topical Terms: </label><br/> Research methods and evaluation<br/> Statistics - Graphic methods<br/> Statistical research
<br/><br/><label>Dewey Class. No.: </label>001.4226  / AND