Mitchell Michael N. Interpreting and Visualizing Regression Models Using Stata

2nd edition. — Stata Press, 2021. — 732 p. — ISBN 978-1-59718-322-2.Interpreting and Visualizing Regression Models Using Stata, Second Edition provides clear and simple examples illustrating how to interpret and visualize a wide variety of regression models. Including over 200 figures, the book illustrates linear models with continuous predictors (modeled linearly, using polynomials, and piecewise), interactions of continuous predictors, categorical predictors, interactions of categorical predictors, and interactions of continuous and categorical predictors. The book also illustrates how to interpret and visualize results from multilevel models, models where time is a continuous predictor, models with time as a categorical predictor, nonlinear models (such as logistic or ordinal logistic regression), and models involving complex survey data. The examples illustrate the use of the margins, marginsplot, contrast, and pwcompare commands. This new edition reflects new and enhanced features added to Stata, most importantly the ability to label statistical output using value labels associated with factor variables. As a result, output regarding marital status is labeled using intuitive labels like Married and Unmarried instead of using numeric values such as 1 and 2. All the statistical output in this new edition capitalizes on this new feature, emphasizing the interpretation of results based on variables labeled using intuitive value labels. Additionally, this second edition illustrates other new features, such as using transparency in graphics to more clearly visualize overlapping confidence intervals and using small sample-size estimation with mixed models. If you ever find yourself wishing for simple and straightforward advice about how to interpret and visualize regression models using Stata, this book is for you.Continuous predictors
Continuous predictors: Linear
Continuous predictors: Polynomials
Continuous predictors: Piecewise models
Continuous by continuous interactions
Continuous by continuous by continuous interactions
Categorical predictors
Categorical predictors
Categorical by categorical interactions
Categorical by categorical by categorical interactions
Continuous and categorical predictors
Linear by categorical interactions
Polynomial by categorical interactions
Piecewise by categorical interactions
Continuous by continuous by categorical interactions
Continuous by categorical by categorical interactions
Beyond ordinary linear regression
Multilevel models
Time as a continuous predictor
Time as a categorical predictor
Nonlinear models
Complex survey data
Customizing output from estimation commands
The margins command
The marginsplot command
The contrast command
The pwcompare command