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REGRESSION MODELS FOR CATEGORICAL DEPENDENT VARIABLES USING STATA-PAPERBAC K BOOK
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Bevindt zich in: Bellingham, Massachusetts, Verenigde Staten
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Specificaties
- Objectstaat
- ISBN
- 9781597181112
- Publication Year
- 2014
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Publication Name
- Regression Models for Categorical Dependent Variables Using Stata, Third Edition
- Item Height
- 1.5in
- Item Length
- 9.3in
- Publisher
- Statacorp LLC
- Item Width
- 7.2in
- Item Weight
- 43.3 Oz
- Number of Pages
- 589 Pages
Over dit product
Product Information
Regression Models for Categorical Dependent Variables Using Stata, Third Editionshows how to use Stata to fit and interpret regression models for categorical data. The third edition is a complete rewrite of the book. Factor variables and the margins command changed how the effects of variables can be estimated and interpreted. In addition, the authors' views on interpretation have evolved. The changes to Stata and to the authors' views inspired the authors to completely rewrite their popular SPost commands to take advantage of the power of the margins command and the flexibility of factor-variable notation. The new edition will interest readers of a previous edition as well as new readers. Even though about 150 pages of appendixes were removed, the third edition is about 60 pages longer than the second. Although regression models for categorical dependent variables are common, few texts explain how to interpret such models; this text fills the void. With the book, Long and Freese provide a suite of commands for model interpretation, hypothesis testing, and model diagnostics. The new commands that accompany the third edition make it easy to include powers or interactions of covariates in regression models and work seamlessly with models estimated with complex survey data. The authors' new commands greatly simplify the use of margins , in the same way that the marginsplot command harnesses the power of margins for plotting predictions. The authors discuss how to use margins and their new mchange , mtable , and mgen commands to compute tables and to plot predictions. They also discuss how to use these commands to estimate marginal effects, averaged either over the sample or at fixed values of the regressors. The authors introduce and advocate a variety of new methods that use predictions to interpret the effect of variables in regression models. The third edition begins with an excellent introduction to Stata and follows with general treatments of the estimation, testing, fit, and interpretation of this class of models. New to the third edition is an entire chapter about how to interpret regression models using predictions--a chapter that is expanded upon in later chapters that focus on models for binary, ordinal, nominal, and count outcomes. Long and Freese use many concrete examples in their third edition. All the examples, datasets, and author-written commands are available on the authors' website, so readers can easily replicate the examples with Stata. This book is ideal for students or applied researchers who want to learn how to fit and interpret models for categorical data.
Product Identifiers
Publisher
Statacorp LLC
ISBN-10
1597181110
ISBN-13
9781597181112
eBay Product ID (ePID)
219724383
Product Key Features
Publication Name
Regression Models for Categorical Dependent Variables Using Stata, Third Edition
Format
Trade Paperback
Language
English
Publication Year
2014
Type
Textbook
Number of Pages
589 Pages
Dimensions
Item Length
9.3in
Item Height
1.5in
Item Width
7.2in
Item Weight
43.3 Oz
Additional Product Features
Lc Classification Number
Qa278.2.L66 2014
Edition Description
Revised Edition,New Edition
Edition Number
3
Reviews
"...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 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, "...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
Table of Content
General information. Introduction. Introduction to Stata. Estimation, testing, and fit. Methods of interpretation. Models for specific kinds of outcomes. Models for binary outcomes: Estimation, testing, and fit. Models for binary outcomes: Interpretation. Models for ordinal outcomes. Models for nominal outcomes. Models for count outcomes.
Copyright Date
2014
Target Audience
College Audience
Topic
Probability & Statistics / General, Probability & Statistics / Regression Analysis, Algebra / General
Lccn
2014-948009
Dewey Decimal
519.536
Dewey Edition
23
Illustrated
Yes
Genre
Mathematics
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