Автор: Liu, Xing Название: Categorical data analysis and multilevel modeling using r ISBN: 1544324901 ISBN-13(EAN): 9781544324906 Издательство: Sage Publications Рейтинг: Цена: 120390.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Categorical Data Analysis and Multilevel Modeling Using Rprovides a practical guide to regression techniques for analyzing binary, ordinal, nominal, and count response variables using the R software. Author Xing Liu offers a unified framework for both single-level and multilevel modeling of categorical and count response variables with both frequentist and Bayesian approaches. Each chapter demonstrates how to conduct the analysis using R, how to interpret the models, and how to present the results for publication. A companion website for this book contains datasets and R commands used in the book for students, and solutions for the end-of-chapter exercises on the instructor site.
Автор: Erling B. Andersen Название: The Statistical Analysis of Categorical Data ISBN: 364278819X ISBN-13(EAN): 9783642788192 Издательство: Springer Рейтинг: Цена: 102480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
Автор: Xu, Jun Название: Modern applied regressions ISBN: 0367173875 ISBN-13(EAN): 9780367173876 Издательство: Taylor&Francis Рейтинг: Цена: 91860.00 T Наличие на складе: Нет в наличии. Описание: Modern Applied Regressions creates an intricate mural with mosaics of categorical and limited response variable (CLRV) models using both Bayesian and Frequentist approaches. Written for graduate students, junior researchers, and quantitative analysts in behavioral, health, and social sciences.
Автор: Hoffmann John P. Название: Regression Models for Categorical, Count, and Related Variables: An Applied Approach ISBN: 0520289293 ISBN-13(EAN): 9780520289291 Издательство: Wiley Рейтинг: Цена: 58080.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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. This book provides an introduction and overview of several statistical models designed for these types of outcomes.
Автор: Wicher Bergsma; Marcel A. Croon; Jacques A. Hagena Название: Marginal Models ISBN: 1441918736 ISBN-13(EAN): 9781441918734 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Marginal models are often the best way of answering research questions involving dependent observations. This comprehensive overview of the basic principles of marginal modeling offers a wide range of possible applications through many real world examples.
Автор: Helge Toutenburg Название: Experimental Design and Model Choice ISBN: 3642525008 ISBN-13(EAN): 9783642525001 Издательство: Springer Рейтинг: Цена: 81050.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Автор: Powers, Daniel Xie, Yu Название: Statistical methods for categorical data analysis ISBN: 0123725623 ISBN-13(EAN): 9780123725622 Издательство: Emerald Рейтинг: Цена: 77230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides a comprehensive introduction to methods and models for categorical data analysis and their applications in social science research. Companion website also available, at https://webspace.utexas.edu/dpowers/www/
Автор: Agresti Alan Название: Categorical Data Analysis ISBN: 0470463635 ISBN-13(EAN): 9780470463635 Издательство: Wiley Рейтинг: Цена: 134060.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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.
Автор: Roverato Alberto Название: Graphical Models for Categorical Data ISBN: 1108404960 ISBN-13(EAN): 9781108404969 Издательство: Cambridge Academ Рейтинг: Цена: 31670.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: For advanced students of network data science, this compact account covers both well-established methodology and the theory of models recently introduced in the graphical model literature. It focuses on the discrete case where all variables involved are categorical and, in this context, it achieves a unified presentation of classical and recent results.
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