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This book provides a selection of modern and sophisticated
methodologies for the analysis of large and complex univariate and
multivariate categorical data. It gives an overview of a
substantive and broad collection of topics in the analysis of
categorical data, including association, marginal and graphical
models, time series and fixed effects models, as well as modern
methods of estimation such as regularization, Bayesian estimation
and bias reduction methods, along with new simple measures for
model interpretability. Methodological innovations and developments
are illustrated and explained through real-world applications,
together with useful R packages, allowing readers to replicate most
of the analyses using the provided code. The applications span a
variety of disciplines, including education, psychology, health,
economics, and social sciences.Â
Contingency tables arise in diverse fields, including life
sciences, education, social and political sciences, notably market
research and opinion surveys. Their analysis plays an essential
role in gaining insight into structures of the quantities under
consideration and in supporting decision making. Combining both
theory and applications, this book presents models and methods for
the analysis of two- and multidimensional-contingency tables.An
excellent reference for advanced undergraduates, graduate students,
and practitioners in statistics as well as biosciences, social
sciences, education, and economics, the work may also be used as a
textbook for a course on categorical data analysis. Prerequisites
include basic background on statistical inference and knowledge of
statistical software packages.
Shows the elements of statistical science that are highly relevant
for students who plan to become data scientists less emphasis on
probability theory and methods of probability such as
combinatorics, derivations of probability distributions of
transformations of random variables (except for explanations of t,
chi-squared, and F constructions) Formal statements and proofs of
theorems, and decision theory Introduces some modern topics that do
not normally appear in "math stat" texts but are especially
relevant for data scientists, such as generalized linear models for
non-normal responses (e.g., logistic regression) Bayesian and
regularized fitting of models (e.g., showing an example using the
lasso), classification and clustering, and implementing methods
with modern software (R and Python)
Contingency tables arise in diverse fields, including life
sciences, education, social and political sciences, notably market
research and opinion surveys. Their analysis plays an essential
role in gaining insight into structures of the quantities under
consideration and in supporting decision making. Combining both
theory and applications, this book presents models and methods for
the analysis of two- and multidimensional-contingency tables. An
excellent reference for advanced undergraduates, graduate students,
and practitioners in statistics as well as biosciences, social
sciences, education, and economics, the work may also be used as a
textbook for a course on categorical data analysis. Prerequisites
include basic background on statistical inference and knowledge of
statistical software packages.
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