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Books > Science & Mathematics > Mathematics > Probability & statistics
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Regression - Models, Methods and Applications (Hardcover, 2nd ed. 2021)
Loot Price: R4,666
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Regression - Models, Methods and Applications (Hardcover, 2nd ed. 2021)
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Now in its second edition, this textbook provides an applied and
unified introduction to parametric, nonparametric and
semiparametric regression that closes the gap between theory and
application. The most important models and methods in regression
are presented on a solid formal basis, and their appropriate
application is shown through numerous examples and case studies.
The most important definitions and statements are concisely
summarized in boxes, and the underlying data sets and code are
available online on the book's dedicated website. Availability of
(user-friendly) software has been a major criterion for the methods
selected and presented. The chapters address the classical linear
model and its extensions, generalized linear models, categorical
regression models, mixed models, nonparametric regression,
structured additive regression, quantile regression and
distributional regression models. Two appendices describe the
required matrix algebra, as well as elements of probability
calculus and statistical inference. In this substantially revised
and updated new edition the overview on regression models has been
extended, and now includes the relation between regression models
and machine learning, additional details on statistical inference
in structured additive regression models have been added and a
completely reworked chapter augments the presentation of quantile
regression with a comprehensive introduction to distributional
regression models. Regularization approaches are now more
extensively discussed in most chapters of the book. The book
primarily targets an audience that includes students, teachers and
practitioners in social, economic, and life sciences, as well as
students and teachers in statistics programs, and mathematicians
and computer scientists with interests in statistical modeling and
data analysis. It is written at an intermediate mathematical level
and assumes only knowledge of basic probability, calculus, matrix
algebra and statistics.
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