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Lectures on Categorical Data Analysis, Tam?s Rudas


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Автор: Tam?s Rudas
Название:  Lectures on Categorical Data Analysis
ISBN: 9781493992591
Издательство: Springer
Классификация:


ISBN-10: 1493992597
Обложка/Формат: Soft cover
Страницы: 285
Вес: 0.62 кг.
Дата издания: 2018
Серия: Springer Texts in Statistics
Язык: English
Издание: Softcover reprint of
Иллюстрации: 1 illustrations, black and white; xi, 285 p. 1 illus.
Размер: Book (Paperback Initiative)
Читательская аудитория: Professional & vocational
Основная тема: Statistics
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This book offers a relatively self-contained presentation of the fundamental results in categorical data analysis, which plays a central role among the statistical techniques applied in the social, political and behavioral sciences, as well as in marketing and medical and biological research. The methods applied are mainly aimed at understanding the structure of associations among variables and the effects of other variables on these interactions. A great advantage of studying categorical data analysis is that many concepts in statistics become transparent when discussed in a categorical data context, and, in many places, the book takes this opportunity to comment on general principles and methods in statistics, addressing not only the “how” but also the “why.” Assuming minimal background in calculus, linear algebra, probability theory and statistics, the book is designed to be used in upper-undergraduate and graduate-level courses in the field and in more general statistical methodology courses, as well as a self-study resource for researchers and professionals. The book covers such key issues as: higher order interactions among categorical variables; the use of the delta-method to correctly determine asymptotic standard errors for complex quantities reported in surveys; the fundamentals of the main theories of causal analysis based on observational data; the usefulness of the odds ratio as a measure of association; and a detailed discussion of log-linear models, including graphical models. The book contains over 200 problems, many of which may also be used as starting points for undergraduate research projects. The material can be used by students toward a variety of goals, depending on the degree of theory or application desired.
Дополнительное описание: The Role of Categorical Data Analysis.- Sampling Distributions.- Normal Approximations.- Simple Estimation for Categorical Data.- Basic testing for categorical data.- Association.-Latent classes and exponential families.- Effects and associations.- Simpso


Statistical Analysis of Longitudinal Categorical Data in the Social and Behavioral Sciences

Автор: Von Eye
Название: Statistical Analysis of Longitudinal Categorical Data in the Social and Behavioral Sciences
ISBN: 0805831827 ISBN-13(EAN): 9780805831825
Издательство: Taylor&Francis
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Цена: 46950.00 T
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Описание: This authored book provides a comprehensive resource for analyzing a variety of categorical data.Accessible to social scientists and students, no advanced mathematical training is required. It includes applications,examples,computer tutorials,& exercises

The Statistical Analysis of Categorical Data

Автор: Erling B. Andersen
Название: The Statistical Analysis of Categorical Data
ISBN: 364278819X ISBN-13(EAN): 9783642788192
Издательство: Springer
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Цена: 102480.00 T
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Описание: The aim of this book is to give an up to date account of the most commonly uses statisti- cal models for categorical data. In many cases, the data sets are those data sets, which were not included in the examples of the book, although they at one point in time were regarded as potential can- didates for an example.

Categorical and Nonparametric Data Analysis

Автор: Nussbaum E Michael
Название: Categorical and Nonparametric Data Analysis
ISBN: 1138787825 ISBN-13(EAN): 9781138787827
Издательство: Taylor&Francis
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Цена: 81650.00 T
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Описание: Featuring in-depth coverage of categorical and nonparametric statistics, this book provides a conceptual framework for choosing the most appropriate type of test in various research scenarios. Class tested at the University of Nevada, the book's clear explanations of the underlying assumptions, computer simulations, and Exploring the Concept boxes help reduce reader anxiety. Problems inspired by actual studies provide meaningful illustrations of the techniques. The underlying assumptions of each test and the factors that impact validity and statistical power are reviewed so readers can explain their assumptions and how tests work in future publications. Numerous examples from psychology, education, and other social sciences demonstrate varied applications of the material. Basic statistics and probability are reviewed for those who need a refresher. Mathematical derivations are placed in optional appendices for those interested in this detailed coverage. Highlights include the following: Unique coverage of categorical and nonparametric statistics better prepares readers to select the best technique for their particular research project; however, some chapters can be omitted entirely if preferred. Step-by-step examples of each test help readers see how the material is applied in a variety of disciplines.  Although the book can be used with any program, examples of how to use the tests in SPSS and Excel foster conceptual understanding. Exploring the Concept boxes integrated throughout prompt students to review key material and draw links between the concepts to deepen understanding.  Problems in each chapter help readers test their understanding of the material.  Emphasis on selecting tests that maximize power helps readers avoid "marginally" significant results.  Website (www.routledge.com/9781138787827) features datasets for the book's examples and problems, and for the instructor, PowerPoint slides, sample syllabi, answers to the even-numbered problems, and Excel data sets for lecture purposes. Intended for individual or combined graduate or advanced undergraduate courses in categorical and nonparametric data analysis, cross-classified data analysis, advanced statistics and/or quantitative techniques taught in psychology, education, human development, sociology, political science, and other social and life sciences, the book also appeals to researchers in these disciplines. The nonparametric chapters can be deleted if preferred. Prerequisites include knowledge of t tests and ANOVA.

The Analysis of Categorical Data Using GLIM

Автор: James K. Lindsey
Название: The Analysis of Categorical Data Using GLIM
ISBN: 0387970290 ISBN-13(EAN): 9780387970295
Издательство: Springer
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Цена: 107130.00 T
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Описание: 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.

A Course in Categorical Data Analysis

Автор: Leonard
Название: A Course in Categorical Data Analysis
ISBN: 1584881801 ISBN-13(EAN): 9781584881803
Издательство: Taylor&Francis
Рейтинг:
Цена: 117390.00 T
Наличие на складе: Невозможна поставка.
Описание: Categorical data requires a different methodology and techniques typically not encountered in introductory statistics courses. This title presents various ways of extracting real-life conclusions from contingency tables. It uses a Fisherian approach to categorical data analysis and incorporates numerous examples and real data sets.

Statistics. Volume 3: Categorical and Time Dependent Data Analysis

Автор: Kunihiro Suzuki
Название: Statistics. Volume 3: Categorical and Time Dependent Data Analysis
ISBN: 1536151246 ISBN-13(EAN): 9781536151244
Издательство: Nova Science
Рейтинг:
Цена: 252370.00 T
Наличие на складе: Невозможна поставка.
Описание: We utilize statistics when we evaluate TV program ratings, predict voting outcomes, prepare stock, predict the amount of sales, and evaluate the effectiveness of medical treatment. We want to predict the results not on the basis of personal experience or images, but on the basis of corresponding data. The accuracy of the prediction depends on the data and related theories. It is easy to show input and output data associated with a model without understanding it. However, the models themselves are not perfect, because they contain assumptions and approximations in general. Therefore, the application of the model to the data should be careful. We should know what model we should apply to the data, what parameters are assumed in the model, and what we can state based on the results of the models.Let us consider a coin toss, for example. When we perform a coin toss, we obtain a head or a tail. If we try the toss a coin three times, we may obtain the results of two heads and one tail. Therefore, the probability that we obtain for heads is , and the one that we obtain for tails is . This is a fact and we need not to discuss this any further. It is important to notice that the probability ( ) of getting a head is limited to this trial. Therefore, we can never say that the probability that we obtain for heads with this coin is , in which we state general characteristics of the coin. If we perform the coin toss trial 400 times and obtain heads 300 times, we may be able to state that the probability of obtaining a head is as the characteristics of the coin. What we can state based on the obtained data depends on the sample number. Statistics gives us a clear guideline under which we can state something is based on the data with corresponding error ranges.Mathematics used in statistics is not so easy. It may be tough work to acquire the related techniques. Fortunately, software development makes it easy to obtain results. Therefore, many members who are not specialists in mathematics can perform statistical analysis with these types of software. However, it is important to understand the meaning of the model, that is, why some certain variables are introduced and what they express, and what we can state based on the results. Therefore, understanding mathematics related to the models is invoked to appreciate the results.In this book, the authors treat models from fundamental ones to advanced ones without skipping their derivation processes. It is then possible to clearly understand the assumptions and approximations used in the models, and hence understand the limitation of the models.The authors also cover almost all the subjects in statistics since they are all related to each other, and the mathematical treatments used in a model are frequently used in the other ones.Additionally, many good practical and theoretical books on statistics are presented [1]-[10]. However, these books are oriented to special cases: Fundamental, mathematical, or special subjects. The author also aims to connect theories to practical subjects. He hopes that this book will aid readers in furthering their knowledge of special cases in statistics.

Categorical Data Analysis by AIC

Автор: Y. Sakamoto
Название: Categorical Data Analysis by AIC
ISBN: 0792314298 ISBN-13(EAN): 9780792314295
Издательство: Springer
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Цена: 88500.00 T
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Описание: Presents a practical approach to categorical data analysis based on the Akaike Information Criterion (AIC) and the Akaike Bayesian Information Criterion (ABIC). Topics covered include variable selection for categorical data, Bayesian binary regression and nonparametric density estimator.

Longitudinal Categorical Data Analysis

Автор: Brajendra C. Sutradhar
Название: Longitudinal Categorical Data Analysis
ISBN: 1493953206 ISBN-13(EAN): 9781493953202
Издательство: Springer
Рейтинг:
Цена: 88500.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is the first book in longitudinal categorical data analysis with parametric correlation models developed based on dynamic relationships among repeated categorical responses.


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