Bayesian Methods for Statistical Analysis
by Borek Puza
Publisher: ANU Press 2015
Number of pages: 697
A book on statistical methods for analysing a wide variety of data. Topics: bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete computer code.
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by Alex Reinhart - refsmmat.com
This is a guide to the most popular statistical errors and slip-ups committed by scientists every day, in the lab and in peer-reviewed journals. It assumes no prior knowledge of statistics, you can read it before your first statistics course.
by David Brink - BookBoon
This compendium of probability and statistics offers an instruction in the central areas of these subjects. The focus is overview. The book is intensively examplefied, which give the reader a recipe how to solve all the common types of exercises.
by David R. Lilja - University of Minnesota
The book presents one of the fundamental data modeling techniques in an informal tutorial style. Learn how to predict system outputs from measured data using a detailed step-by-step process to develop, train, and test reliable regression models.
by David W. Stockburger - Missouri State University
The book for a course in multivariate statistics for first year graduate or advanced undergraduates. It is neither a mathematical treatise nor a cookbook. Instead of complicated mathematical proofs the author wrote about mathematical ideas.