Regression Models For Categorical Dependent Variables Using Stata
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Regression Models For Categorical Dependent Variables Using Stata Second Edition
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Author : J. Scott Long
language : en
Publisher: Stata Press
Release Date : 2006
Regression Models For Categorical Dependent Variables Using Stata Second Edition written by J. Scott Long and has been published by Stata Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Computers categories.
The goal of the book is to make easier to carry out the computations necessary for the full interpretation of regression nonlinear models for categorical outcomes usign Stata.
Regression Models For Categorical Dependent Variables Using Stata
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Author : J. Scott Long
language : en
Publisher:
Release Date : 2006
Regression Models For Categorical Dependent Variables Using Stata written by J. Scott Long and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Regression analysis categories.
Regression Models For Categorical Dependent Variables Using Stata Third Edition
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Author : J. Scott Long
language : en
Publisher: Stata Press
Release Date : 2014-09-10
Regression Models For Categorical Dependent Variables Using Stata Third Edition written by J. Scott Long and has been published by Stata Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-10 with Mathematics categories.
Regression Models for Categorical Dependent Variables Using Stata, Third Edition shows 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.
Regression Models For Categorical And Limited Dependent Variables
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Author : J. Scott Long
language : en
Publisher: SAGE
Release Date : 1997-01-09
Regression Models For Categorical And Limited Dependent Variables written by J. Scott Long and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997-01-09 with Mathematics categories.
Evaluates the most useful models for categorical and limited dependent variables (CLDVs), emphasizing the links among models and applying common methods of derivation, interpretation, and testing. The author also explains how models relate to linear regression models whenever possible. Annotation c.
Data Analysis Using Stata
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Author : Ulrich Kohler (Dr. phil.)
language : en
Publisher: Stata Press
Release Date : 2005-06-15
Data Analysis Using Stata written by Ulrich Kohler (Dr. phil.) and has been published by Stata Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-06-15 with Computers categories.
"This book provides a comprehensive introduction to Stata with an emphasis on data management, linear regression, logistic modeling, and using programs to automate repetitive tasks. Using data from a longitudinal study of private households in Germany, the book presents many examples from the social sciences to bring beginners up to speed on the use of Stata." -- BACK COVER.
Statistical Methods For Categorical Data Analysis
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Author : Daniel Powers
language : en
Publisher: Emerald Group Publishing
Release Date : 2008-11-13
Statistical Methods For Categorical Data Analysis written by Daniel Powers and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-11-13 with Psychology categories.
This book provides a comprehensive introduction to methods and models for categorical data analysis and their applications in social science research. Companion website also available, at https://webspace.utexas.edu/dpowers/www/
Multilevel Modeling In Plain Language
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Author : Karen Robson
language : en
Publisher: SAGE
Release Date : 2015-11-02
Multilevel Modeling In Plain Language written by Karen Robson and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-02 with Social Science categories.
Have you been told you need to do multilevel modeling, but you can′t get past the forest of equations? Do you need the techniques explained with words and practical examples so they make sense? Help is here! This book unpacks these statistical techniques in easy-to-understand language with fully annotated examples using the statistical software Stata. The techniques are explained without reliance on equations and algebra so that new users will understand when to use these approaches and how they are really just special applications of ordinary regression. Using real life data, the authors show you how to model random intercept models and random coefficient models for cross-sectional data in a way that makes sense and can be retained and repeated. This book is the perfect answer for anyone who needs a clear, accessible introduction to multilevel modeling.
Fixed Effects Regression Models
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Author : Paul D. Allison
language : en
Publisher: SAGE Publications
Release Date : 2009-04-22
Fixed Effects Regression Models written by Paul D. Allison and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-22 with Social Science categories.
This book demonstrates how to estimate and interpret fixed-effects models in a variety of different modeling contexts: linear models, logistic models, Poisson models, Cox regression models, and structural equation models. Both advantages and disadvantages of fixed-effects models will be considered, along with detailed comparisons with random-effects models. Written at a level appropriate for anyone who has taken a year of statistics, the book is appropriate as a supplement for graduate courses in regression or linear regression as well as an aid to researchers who have repeated measures or cross-sectional data. Learn more about "The Little Green Book" - QASS Series! Click Here
Applied Ordinal Logistic Regression Using Stata
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Author : Xing Liu
language : en
Publisher: SAGE Publications
Release Date : 2015-09-30
Applied Ordinal Logistic Regression Using Stata written by Xing Liu and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-09-30 with Social Science categories.
The first book to provide a unified framework for both single-level and multilevel modeling of ordinal categorical data, Applied Ordinal Logistic Regression Using Stata helps readers learn how to conduct analyses, interpret the results from Stata output, and present those results in scholarly writing. Using step-by-step instructions, this non-technical, applied book leads students, applied researchers, and practitioners to a deeper understanding of statistical concepts by closely connecting the underlying theories of models with the application of real-world data using statistical software. An open-access website for the book contains data sets, Stata code, and answers to in-text questions.
Interpreting And Visualizing Regression Models Using Stata
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Author : MICHAEL N. MITCHELL
language : en
Publisher: Stata Press
Release Date : 2020-12-18
Interpreting And Visualizing Regression Models Using Stata written by MICHAEL N. MITCHELL and has been published by Stata Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-18 with categories.
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.