Regression Models for Categorical Dependent Variables Using Stata, Third Edition By J. Scott Long, Jeremy Freese

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Regression Models for Categorical Dependent Variables Using Stata, Third Edition
 By J. Scott Long, Jeremy Freese

Regression Models for Categorical Dependent Variables Using Stata, Third Edition By J. Scott Long, Jeremy Freese


Regression Models for Categorical Dependent Variables Using Stata, Third Edition
 By J. Scott Long, Jeremy Freese


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Regression Models for Categorical Dependent Variables Using Stata, Third Edition
 By J. Scott Long, Jeremy Freese

  • Amazon Sales Rank: #281487 in Books
  • Brand: imusti
  • Published on: 2014-09-10
  • Original language: English
  • Number of items: 1
  • Dimensions: 9.00" h x 7.00" w x 1.50" l, .0 pounds
  • Binding: Paperback
  • 589 pages
Features
  • Stata Press

Review

"…a friendly and accessible text that is designed to show a reader starting with no knowledge of Stata how to analyze and interpret regression models with categorical dependent variables―as promised by the title. The book extensively uses a companion add-on package, SPost13, of Stata commands that take fitted regression models and return post processed summaries. These greatly enhance the interpretability of nonlinear regressions. This is a third edition, an extensive rewrite of the second, with the Stata package also being completely reworked to taking advantage of functionality released in Stata 11 and after…A commendable aspect of the book is its focus on model interpretation: the Stata package makes this easy, and the book tells people how to do this final and critical step in their data analysis without undue suffering and without cutting corners. Arguably the most difficult aspect of the shift from ordinary linear models to more general regression models is interpreting the fitted models, particularly with regard to the usual nonlinear link functions. One way forward is to focus on aggregating individual-level predictions; this book showcases this approach, and should be applauded for it."―Luke W. Miratrix, Harvard University, in The American Statistician, March 2016

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