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 Peter Young - arXiv
These notes discuss, in a style intended for physicists, how to average data and fit it to some functional form. I try to make clear what is being calculated, what assumptions are being made, and to give a derivation of results.
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 Pete Kaslik
Contents: Statistical Reasoning; Obtaining Useful Evidence; Examining the Evidence Using Graphs and Statistics; Inferential Theory; Testing Hypotheses; Confidence Intervals and Sample Size; Analysis of Bivariate Quantitative Data; Chi Square; etc.
by Wolfgang K. Hardle, Leopold Simar - Springer
The authors present multivariate data analysis in a way that is understandable to non-mathematicians and practitioners confronted by statistical data analysis. The book has a friendly yet rigorous style. Mathematical results are clearly stated.