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Regression models for categorical, count, and related variables an applied approach [electronic resource]
Hoffmann, John P.
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| 자료유형 | E-BOOK |
|---|---|
| 서명/저자사항 | Regression models for categorical, count, and related variables [electronic resource]: an applied approach/ John P. Hoffmann. |
| 개인저자 | Hoffmann, John P.(John Patrick), 1962- author, |
| 발행사항 | Oakland, California: University of California Press, [2016]. |
| 형태사항 | 1 online resource. |
| 기타형태 저록 | Print version: Hoffmann, John P. (John Patrick), 1962- author. Regression models for categorical, count, and related variables Oakland, California : University of California Press, [2016] 9780520289291 |
| ISBN | 9780520965492 0520965493 |
| 서지주기 | Includes bibliographical references and index. |
| 내용주기 | Review of linear regression models -- Categorical data and generalized linear models -- Logistic and probit regression models -- Ordered logistic and probit regression models -- Multinomial logistic and probit regression models -- Poisson and negative binomial regression models -- Event history models -- Regression models for longitudinal data -- Multilevel regression models -- Principal components and factor analysis -- Appendix A : SAS, SPSS, and R code for examples in chapters -- Appendix B : using simulations to examine assumptions of OLS regression -- Appendix C : working with missing data. |
| 요약 | "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 under 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"--Provided by publisher. |
| 일반주제명 | Regression analysis --Mathematical models. Regression analysis --Computer programs. Social sciences --Statistical methods. MATHEMATICS / Applied MATHEMATICS / Probability & Statistics / General Regression analysis --Computer programs. Regression analysis --Mathematical models. Social sciences --Statistical methods. |
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