Author Mitchell, Michael N. Additional authorUnknown Publisher Stata Press ([s.l], 2021) Language(eng) English ISBN9781597183222 SeriesUnknown Edition2nd ed. SubjectSTATA; NoteUnknown AbstractMichael Mitchell's Interpreting and Visualizing Regression Models Using Stata, Second Edition is a clear treatment of how to carefully present results from model-fitting in a wide variety of settings. It is a boon to anyone who has to present the tangible meaning of a complex model clearly, regardless of the audience. As an example, many experienced researchers start to squirm when asked to give a simple explanation of the practical meaning of interactions in nonlinear models such as logistic regression. The techniques presented in Mitchell's book make answering those questions easy. The overarching theme of the book is that graphs make interpreting even the most complicated models containing interaction terms, categorical variables, and other intricacies straightforward. Using a dataset based on the General Social Survey, Mitchell starts with a basic linear regression with a single independent variable and then illustrates how to tabulate and graph predicted values. Mitchell focuses on Stata's margins and marginsplot commands, which play a central role in the book and which greatly simplify the calculation and presentation of results from regression models. In particular, through use of the marginsplot command, he shows how you can graphically visualize every model presented in the book and thus gain insight into results much easier when you can view them in a graph rather than in a mundane table of results. Mitchell then proceeds to more complicated models where the effects of the independent variables are nonlinear. After discussing how to detect nonlinear effects, he presents examples using both standard polynomial models, where independent variables can be raised to powers like -1 or 1/2. In all cases, Mitchell again uses the marginsplot command to illustrate the effect that changing an independent variable has on the dependent variable. Piecewise linear models are presented as well; these are linear models in which the slope or intercept is allowed to change depending on the range of an independent variable. He also uses the contrast command when discussing categorical variables; as the name suggests, this command allows you to easily contrast predictions made for various levels of the categorical variable. Interaction terms can be tricky to interpret, but Mitchell shows how graphs produced by marginsplot greatly clarify results.

Number of pages1 online resource (xxx, 610 p.) DimensionUnknown Other descriptionUnknown