Presenting statistical results effectively. (2017)

• Record Type: Book
• Further Information: Note: Robert Stanley Andersen, David A. Armstrong II.
• Authors: Ph. D, Andersen, Robert; II, Armstrong, David A, 1976-
• Contents: 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;
• Edition: 1st
• Publisher Details: Los Angeles : SAGE
• Publication Date: 2017
• Extent: 1 online resource
• Subjects: 001.4226; Statistics -- Graphic methods
• Languages: English
• ISBNs: 9781473944176
• Notes: Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.