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A Course in Categorical Data Analysis, Leonard



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Автор: Leonard
Название:  A Course in Categorical Data Analysis
Перевод названия: Курс по категорическому анализу данных
ISBN: 9781584881803
Издательство: Taylor&Francis
Классификация:
ISBN-10: 1584881801
Обложка/Формат: Paperback
Страницы: 208
Вес: 0.3 кг.
Дата издания: November 22, 1999
Серия: Chapman & hall/crc texts in statistical science
Язык: English
Иллюстрации: 138 black & white tables
Размер: 24.03 x 15.04 x 1.22
Читательская аудитория: Postgraduate, research & scholarly
Ссылка на Издательство: Link
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Поставляется из: Англии
Дополнительное описание: Кол-во стр.: 208
Формат: 229 x 152
Дата издания: 1999
Страна : UK
Круг читателей: undergraduate; postgraduate; research, professiona
Вес: 295
Серия : Texts in Statistical Science Series





Categorical Data Analysis

Автор: Agresti Alan
Название: Categorical Data Analysis
ISBN: 0470463635 ISBN-13(EAN): 9780470463635
Издательство: Wiley
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Цена: 18970 р.
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Описание: Praise for the Second Edition "A must-have book for anyone expecting to do research and/or applications in categorical data analysis. " Statistics in Medicine "It is a total delight reading this book.

Introduction to the Statistical Analysis of Categorical Data

Автор: Andersen
Название: Introduction to the Statistical Analysis of Categorical Data
ISBN: 354062399X ISBN-13(EAN): 9783540623991
Издательство: Springer
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Цена: 6578 р.
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Описание: This book deals with the analysis of categorical data. Statistical models, especially log-linear models for contingency tables and logistic regression, are described and applied to real life data. Special emphasis is given to the use of graphical methods.

Categorical Data Analysis by Example

Автор: Upton
Название: Categorical Data Analysis by Example
ISBN: 1119307864 ISBN-13(EAN): 9781119307860
Издательство: Wiley
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Цена: 10993 р.
Наличие на складе: Поставка под заказ.

Описание: Introduces the key concepts in the analysis of categoricaldata with illustrative examples and accompanying R code This book is aimed at all those who wish to discover how to analyze categorical data without getting immersed in complicated mathematics and without needing to wade through a large amount of prose.

Analysis of Categorical Data with R

Автор: Bilder
Название: Analysis of Categorical Data with R
ISBN: 1439855676 ISBN-13(EAN): 9781439855676
Издательство: Taylor&Francis
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Цена: 12374 р.
Наличие на складе: Поставка под заказ.

Описание: Learn How to Properly Analyze Categorical DataAnalysis of Categorical Data with R presents a modern account of categorical data analysis using the popular R software. It covers recent techniques of model building and assessment for binary, multicategory, and count response variables and discusses fundamentals, such as odds ratio and probability estimation. The authors give detailed advice and guidelines on which procedures to use and why to use them. The Use of R as Both a Data Analysis Method and a Learning ToolRequiring no prior experience with R, the text offers an introduction to the essential features and functions of R. It incorporates numerous examples from medicine, psychology, sports, ecology, and other areas, along with extensive R code and output. The authors use data simulation in R to help readers understand the underlying assumptions of a procedure and then to evaluate the procedure’s performance. They also present many graphical demonstrations of the features and properties of various analysis methods. Web ResourceThe data sets and R programs from each example are available at www.chrisbilder.com/categorical. The programs include code used to create every plot and piece of output. Many of these programs contain code to demonstrate additional features or to perform more detailed analyses than what is in the text. Designed to be used in tandem with the book, the website also uniquely provides videos of the authors teaching a course on the subject. These videos include live, in-class recordings, which instructors may find useful in a blended or flipped classroom setting. The videos are also suitable as a substitute for a short course.

Categorical Data Analysis, 2nd Edition

Автор: Alan Agresti
Название: Categorical Data Analysis, 2nd Edition
ISBN: 0471360937 ISBN-13(EAN): 9780471360933
Издательство: Wiley
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Цена: 13750 р.
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Описание: Responding to the developments in the field as well as to the needs of a new generation of professionals and students, this edition offers a comprehensive introduction to the most important methods for categorical data analysis. It is suitable for statisticians and biostatisticians as well as scientists and graduate students practicing statistics.

Analysis of ordinal categorical data 2nd

Автор: Agresti, Alan
Название: Analysis of ordinal categorical data 2nd
ISBN: 0470082895 ISBN-13(EAN): 9780470082898
Издательство: Wiley
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Цена: 17318 р.
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Описание: Statistical science s first coordinated manual of methods for analyzing ordered categorical data, now fully revised and updated, continues to present applications and case studies in fields as diverse as sociology, public health, ecology, marketing, and pharmacy.

Applied Categorical and Count Data Analysis

Название: Applied Categorical and Count Data Analysis
ISBN: 1439806241 ISBN-13(EAN): 9781439806241
Издательство: Taylor&Francis
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Цена: 11686 р.
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Описание: Developed from the authors’ graduate-level biostatistics course, Applied Categorical and Count Data Analysis explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. 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 using rigorous mathematical arguments. The text covers classic concepts and popular topics, such as contingency tables, logistic 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. 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.

Categorical Data Analysis

Автор: Yang Keming
Название: Categorical Data Analysis
ISBN: 1446266516 ISBN-13(EAN): 9781446266519
Издательство: Sage Publications
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Цена: 102028 р.
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Описание: These four volumes provide a collection  of key publications on categorical data analysis, carefully put together so that the reader can easily navigate, understand and put in context the major concepts and methods of analysing categorical data. The major work opens with a series of papers that address general issues in CDA, and progresses with publications which follow a logical movement from the statistics for analysing a single categorical variable, to those for studying the relationships between two and more categorical variables, and to categorical variables in some of more advanced methods, such as latent class analysis. Edited and introduced by a leading voice in the field, this collection helpfully includes both theoretical and applied items on its theme, in order to help the reader understand the methods and use them in empirical research. Volume 1: Basic Concepts and Principles Volume 2: Statistical Methods for Analysing Associations Volume 3: Log-Linear and Logistic Regression Models  Volume 4: Advanced and Graphical Statistical Methods

Bayesian Models for Categorical Data

Автор: Peter Congdon
Название: Bayesian Models for Categorical Data
ISBN: 0470092378 ISBN-13(EAN): 9780470092378
Издательство: Wiley
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Цена: 12306 р.
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Описание: The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike. Emphasizing the use of statistical computing and applied data analysis, this book provides a comprehensive introduction to Bayesian methods of categorical outcomes. Reviews recent Bayesian methodology for categorical outcomes (binary, count and multinomial data). Considers missing data models techniques and non-standard models (ZIP and negative binomial). Evaluates time series and spatio-temporal models for discrete data. Features discussion of univariate and multivariate techniques. Provides a set of downloadable worked examples with documented WinBUGS code, available from an ftp site. The author's previous 2 bestselling titles provided a comprehensive introduction to the theory and application of Bayesian models. Bayesian Models for Categorical Data continues to build upon this foundation by developing their application to categorical, or discrete data - one of the most common types of data available. The author's clear and logical approach makes the book accessible to a wide range of students and practitioners, including those dealing with categorical data in medicine, sociology, psychology and epidemiology.

Regression for Categorical Data

Автор: Tutz
Название: Regression for Categorical Data
ISBN: 1107009650 ISBN-13(EAN): 9781107009653
Издательство: Cambridge Academ
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Цена: 9311 р.
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Описание: 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, which provide excellent tools for prediction and the handling of both nominal and ordered categorical predictors. The book is accompanied by an R package that contains data sets and code for all the examples.

Analyzing Categorical Data

Автор: Simonoff Jeffrey S.
Название: Analyzing Categorical Data
ISBN: 0387007490 ISBN-13(EAN): 9780387007496
Издательство: Springer
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Цена: 11549 р.
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Описание: Categorical data arise often in many fields, including biometrics, economics, management, manufacturing, marketing, psychology, and sociology. This book provides an introduction to the analysis of such data. The coverage is broad, using the loglinear Poisson regression model and logistic binomial regression models as the primary engines for methodology. Topics covered include count regression models, such as Poisson, negative binomial, zero-inflated, and zero-truncated models; loglinear models for two-dimensional and multidimensional contingency tables, including for square tables and tables with ordered categories; and regression models for two-category (binary) and multiple-category target variables, such as logistic and proportional odds models.All methods are illustrated with analyses of real data examples, many from recent subject area journal articles. These analyses are highlighted in the text, and are more detailed than is typical, providing discussion of the context and background of the problem, model checking, and scientific implications. More than 200 exercises are provided, many also based on recent subject area literature. Data sets and computer code are available at a web site devoted to the text. Adopters of this book may request a solutions manual from: textbooks@springer-ny.com. Jeffrey S. Simonoff is Professor of Statistics at New York University. He is author of Smoothing Methods in Statistics and coauthor of A Casebook for a First Course in Statistics and Data Analysis, as well as numerous articles in scholarly journals. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an Elected Member of the International Statistical Institute.

Generalized Linear Models for Categorical and Continuous Limited Dependent Variables

Автор: Smithson
Название: Generalized Linear Models for Categorical and Continuous Limited Dependent Variables
ISBN: 1466551739 ISBN-13(EAN): 9781466551732
Издательство: Taylor&Francis
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Цена: 12374 р.
Наличие на складе: Невозможна поставка.

Описание: Generalized Linear Models for Categorical and Continuous Limited Dependent Variables is designed for graduate students and researchers in the behavioral, social, health, and medical sciences. It incorporates examples of truncated counts, censored continuous variables, and doubly bounded continuous variables, such as percentages. The book provides broad, but unified, coverage, and the authors integrate the concepts and ideas shared across models and types of data, especially regarding conceptual links between discrete and continuous limited dependent variables. The authors argue that these dependent variables are, if anything, more common throughout the human sciences than the kind that suit linear regression. They cover special cases or extensions of models, estimation methods, model diagnostics, and, of course, software. They also discuss bounded continuous variables, boundary-inflated models, and methods for modeling heteroscedasticity. Wherever possible, the authors have illustrated concepts, models, and techniques with real or realistic datasets and demonstrations in R and Stata, and each chapter includes several exercises at the end. The illustrations and exercises help readers build conceptual understanding and fluency in using these techniques. At several points the authors bring together material that has been previously scattered across the literature in journal articles, software package documentation files, and blogs. These features help students learn to choose the appropriate models for their purpose.


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