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Providing a much-needed bridge between elementary statistics
courses and advanced research methods courses, Understanding
Advanced Statistical Methods helps students grasp the fundamental
assumptions and machinery behind sophisticated statistical topics,
such as logistic regression, maximum likelihood, bootstrapping,
nonparametrics, and Bayesian methods. The book teaches students how
to properly model, think critically, and design their own studies
to avoid common errors. It leads them to think differently not only
about math and statistics but also about general research and the
scientific method. With a focus on statistical models as producers
of data, the book enables students to more easily understand the
machinery of advanced statistics. It also downplays the
"population" interpretation of statistical models and presents
Bayesian methods before frequentist ones. Requiring no prior
calculus experience, the text employs a "just-in-time" approach
that introduces mathematical topics, including calculus, where
needed. Formulas throughout the text are used to explain why
calculus and probability are essential in statistical modeling. The
authors also intuitively explain the theory and logic behind real
data analysis, incorporating a range of application examples from
the social, economic, biological, medical, physical, and
engineering sciences. Enabling your students to answer the why
behind statistical methods, this text teaches them how to
successfully draw conclusions when the premises are flawed. It
empowers them to use advanced statistical methods with confidence
and develop their own statistical recipes. Ancillary materials are
available on the book's website.
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