Understanding Advanced Statistical Methods

by Peter Westfall, Kevin S. S. Henning

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.

569 pages, Hardcover

First published April 9, 2013

Reviews

[Lucille Nguyen · Rating 5 out of 5]

"Model produces data, model has unknown parameters, data reduce the uncertainty about the unknown parameters."

This book presents a view of statistics based in data-generating processes that is model-centric. Perhaps familiar to readers used to information-theoretic statistics like Burnham-Anderson or Akaike. Model selection as a central principle working with data is an intuitive one for readers.

A useful bridge between easy and advanced statistics, well-explained and demonstrated to the reader. Highly recommend.