Peter Westfall, Kevin S. S. Henning

April 9, 2013
by Chapman and Hall/CRC

Textbook
- 569 Pages
- 220 B/W Illustrations

ISBN 9781466512108 - CAT# K14873

Series: Chapman & Hall/CRC Texts in Statistical Science

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- Shows students how a statistical model is a recipe for producing random data
- Provides a self-contained treatment of mathematical statistics, requiring no prerequisite of calculus and assuming familiarity with algebra, functions, graphs, and spreadsheet software
- Defines and uses the "process" terminology
- Helps students understand how logical conclusions follow from the assumptions—always encouraging them to ask
*why* - Teaches Bayesian methods before classical (frequentist) methods, which provides a seamless transition from probability to likelihood to Bayes as well as enables a well-rounded and thoughtful discussion on the frequentist-based confidence interval and hypothesis testing concepts

**Pedagogical Features**

- Provides vocabulary terms in bold, with definitions summarized at the end of each chapter
- Presents important formulas at the end of every chapter and gives reasons for each step of the derivations
- Includes end-of-chapter exercises essential to understanding the material
- Offers computer code, sample quizzes, exams, and other supplements on the book’s website

*Solutions manual available upon qualifying course adoption** *

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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