Regression Models for Categorical, Count, and Related Variables - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Regression Models for Categorical, Count, and Related Variables write by John P. Hoffmann. This book was released on 2016-08-16. Regression Models for Categorical, Count, and Related Variables available in PDF, EPUB and Kindle. Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or have been collected over time. Sociologists examining the likelihood of interracial marriage, political scientists studying voting behavior, criminologists counting the number of offenses people commit, health scientists studying the number of suicides across neighborhoods, and psychologists modeling mental health treatment success are all interested in outcomes that are not continuous. Instead, they must measure and analyze these events and phenomena in a discrete manner. This book provides an introduction and overview of several statistical models designed for these types of outcomes—all presented with the assumption that the reader has only a good working knowledge of elementary algebra and has taken introductory statistics and linear regression analysis. Numerous examples from the social sciences demonstrate the practical applications of these models. The chapters address logistic and probit models, including those designed for ordinal and nominal variables, regular and zero-inflated Poisson and negative binomial models, event history models, models for longitudinal data, multilevel models, and data reduction techniques such as principal components and factor analysis. Each chapter discusses how to utilize the models and test their assumptions with the statistical software Stata, and also includes exercise sets so readers can practice using these techniques. Appendices show how to estimate the models in SAS, SPSS, and R; provide a review of regression assumptions using simulations; and discuss missing data. A companion website includes downloadable versions of all the data sets used in the book.
Regression for Categorical Data
Regression for Categorical Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Regression for Categorical Data write by Gerhard Tutz. This book was released on 2011-11-21. Regression for Categorical Data available in PDF, EPUB and Kindle. This book introduces basic and advanced concepts of categorical regression with a focus on the structuring constituents of regression, including regularization techniques to structure predictors. In addition to standard methods such as the logit and probit model and extensions to multivariate settings, the author presents more recent developments in flexible and high-dimensional regression, which allow weakening of assumptions on the structuring of the predictor and yield fits that are closer to the data. A generalized linear model is used as a unifying framework whenever possible in particular parametric models that are treated within this framework. Many topics not normally included in books on categorical data analysis are treated here, such as nonparametric regression; selection of predictors by regularized estimation procedures; ternative models like the hurdle model and zero-inflated regression models for count data; and non-standard tree-based ensemble methods. The book is accompanied by an R package that contains data sets and code for all the examples.
Regression Models for Categorical and Count Data
Regression Models for Categorical and Count Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Regression Models for Categorical and Count Data write by Peter Martin. This book was released on 2022-03-01. Regression Models for Categorical and Count Data available in PDF, EPUB and Kindle. This text provides practical guidance on conducting regression analysis on categorical and count data. Step by step and supported by lots of helpful graphs, it covers both the theoretical underpinnings of these methods as well as their application, giving you the skills needed to apply them to your own research. It offers guidance on: · Using logistic regression models for binary, ordinal, and multinomial outcomes · Applying count regression, including Poisson, negative binomial, and zero-inflated models · Choosing the most appropriate model to use for your research · The general principles of good statistical modelling in practice Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey
Regression Models for Categorical and Limited Dependent Variables
Regression Models for Categorical and Limited Dependent Variables - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Regression Models for Categorical and Limited Dependent Variables write by J. Scott Long. This book was released on 1997-01-09. Regression Models for Categorical and Limited Dependent Variables available in PDF, EPUB and Kindle. 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.
Applied Categorical and Count Data Analysis
Applied Categorical and Count Data Analysis - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Applied Categorical and Count Data Analysis write by Wan Tang. This book was released on 2023-04-06. Applied Categorical and Count Data Analysis available in PDF, EPUB and Kindle. Developed from the authors’ graduate-level biostatistics course, Applied Categorical and Count Data Analysis, Second Edition explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. The authors have been teaching categorical data analysis courses at the University of Rochester and Tulane University for more than a decade. This book embodies their decade-long experience and insight in teaching and applying statistical models for categorical and count data. The authors describe the basic ideas underlying each concept, model, and approach to give readers a good grasp of the fundamentals of the methodology without relying on rigorous mathematical arguments. The second edition covers classic concepts and popular topics, such as contingency tables, logistic regression models, and Poisson regression models, along with modern areas that include models for zero-modified count outcomes, parametric and semiparametric longitudinal data analysis, reliability analysis, and methods for dealing with missing values. As in the first edition, R, SAS, SPSS, and Stata programming codes are provided for all the examples, enabling readers to immediately experiment with the data in the examples and even adapt or extend the codes to fit data from their own studies. Designed for a one-semester course for graduate and senior undergraduate students in biostatistics, this self-contained text is also suitable as a self-learning guide for biomedical and psychosocial researchers. It will help readers analyze data with discrete variables in a wide range of biomedical and psychosocial research fields. Features: Describes the basic ideas underlying each concept and model Includes R, SAS, SPSS and Stata programming codes for all the examples Features significantly expanded Chapters 4, 5, and 8 (Chapters 4-6, and 9 in the second edition Expands discussion for subtle issues in longitudinal and clustered data analysis such as time varying covariates and comparison of generalized linear mixed-effect models with GEE
