Models of Category Counts PDF Download

Author: Bernard Fingleton
Publisher: CUP Archive
ISBN: 9780521272834
Category : Computers
Languages : en
Pages : 204

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Book Description
There has been a surge of interest in methods of analysing data that typically arise from surveys of various kinds of experiments in which the number of people, animals, places or objects occupying various categories are counted. In this textbook, first published in 1984, Dr Fingleton describes some techniques centred on the log-linear model from the perspective of the social, behavioural and environmental scientist.

Author: John P. Hoffmann
Publisher: Univ of California Press
ISBN: 0520289293
Category : Mathematics
Languages : en
Pages : 428

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Book Description
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.

Author: Peter Martin
Publisher: SAGE
ISBN: 1529762677
Category : Social Science
Languages : en
Pages : 184

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Book Description
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

Author: Jason W. Osborne
Publisher: SAGE Publications
ISBN: 1506302750
Category : Psychology
Languages : en
Pages : 489

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Book Description
In a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. Author Jason W. Osborne returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.

Author: James K. Lindsey
Publisher: Springer Science & Business Media
ISBN: 1468474480
Category : Mathematics
Languages : en
Pages : 173

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Book Description
The present text is the result of teaching a third year statistical course to undergraduate social science students. Besides their previous statistics courses, these students have had an introductory course in computer programming (FORTRAN, Pascal, or C) and courses in calculus and linear algebra, so that they may not be typical students of sociology. This course on the analysis of contingency tables has been given with all students in front of computer terminals, and, more recently, micro computers, working interactively with GLIM. Given the importance of the analysis of categorical data using log linear models within the overall body of models known as general linear models (GLMs) treated by GLIM, this book should be of interest to anyone, in any field, concerned with such applications. It should be suitable as a manual for applied statistics courses covering this subject. I assume that the reader has already a reasonably strong foundation in statistics, and specifically in dealing with the log-linearllogistic models. I also assume that he or of GLIM itself. In she has access to the GLIM manual and to an operational version other words, this book does not pretend to present either a complete introduction to the use of GLIM or an exposition of the statistical properties of log-linearllogistic models. For the former, I would recommend Healy (1988) and Aitkin et al (1989). Por the latter, many books already exist, of which I would especially recommend that of Pingleton (1984) in the present context.

Author: Alan Agresti
Publisher: John Wiley & Sons
ISBN: 1118730038
Category : Mathematics
Languages : en
Pages : 471

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Book Description
A valuable overview of the most important ideas and results in statistical modeling Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding. The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features: An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methods An overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problems Numerous examples that use R software for all text data analyses More than 400 exercises for readers to practice and extend the theory, methods, and data analysis A supplementary website with datasets for the examples and exercises An invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.

Author: Joseph M. Hilbe
Publisher: Cambridge University Press
ISBN: 1107028337
Category : Business & Economics
Languages : en
Pages : 301

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Book Description
This book provides guidelines and fully worked examples of how to select, construct, interpret and evaluate the full range of count models.

Author: Louis J. Gross
Publisher: American Mathematical Soc.
ISBN: 9780821897126
Category :
Languages : en
Pages : 284

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Book Description
Distinguishing itself among other books on mathematics in plant biology, this book is unique in that it presents a broad overview of how plant biologists are currently utilizing mathematics in their research, and the only one to particularly emphasize plant ecology. Each article is unified by an attempt to tie models at one level of organization to an understanding at other levels. This approach strengthens the connections between theoretical development and observable biology, facilitating the testing of new predictions. Intended for mathematicians, plant biologists and ecologists alike, this book requires only a basic knowledge of differential equations, linear algebra and mathematical modeling; a knowledge of plant biology is helpful. Readers will gain a perspective on what types of biological systems can benefit from mathematical treatment and an appreciation of the current important problems in plant biology.

Author: Wan Tang
Publisher: CRC Press
ISBN: 1000864022
Category : Mathematics
Languages : en
Pages : 1699

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Book Description
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

Author: Dominique Lord
Publisher: Emerald Group Publishing
ISBN: 1786352230
Category : Transportation
Languages : en
Pages : 511

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Book Description
This book increases the level of knowledge on road safety contexts, issues and challenges; shares what can currently be done to address the variety of issues; and points to what needs to be done to make further gains in road safety.