19 Statistics

19.1 Common statistical tests are linear models: a work through

Steve Doogue

This is a reworking of the book Common statistical tests are linear models (or: how to teach stats), written by Jonas Lindeløv. The book beautifully demonstrates how many common statistical tests (such as the t-test, ANOVA and chi-squared) are special cases of the linear model. The book also demonstrates that many non-parametric tests, which are needed when certain test assumptions do not hold, can be approximated by linear models using the rank of values.


19.2 Learning statistics with R: A tutorial for psychology students and other beginners. (Version 0.6.1)

Danielle Navarro

Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. The book discusses how to get started in R as well as giving an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book.

The book is free online.


19.3 Answering questions with data

Matthew J. Crump

This is a free textbook teaching introductory statistics for undergraduates in Psychology. This textbook is part of a larger OER course package for teaching undergraduate statistics in Psychology, including this textbook, a lab manual, and a course website.

Looks like a comprehensive stats resource!


19.4 Mixed Models with R : Getting started with random effects

Michael Clark

Mixed models are an extremely useful modeling tool for situations in which there is some dependency among observations in the data, where the correlation typically arises from the observations being clustered in some way.


19.5 An Introduction to Statistical and Data Sciences via R

Chester Ismay and Albert Kim

An incredibly beginner friendly introduction to both datascience and statistics concepts as well as R.

The book is free to read online.


19.6 Statistical Rethinking

A Bayesian Course with Examples in R and Stan

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today’s model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work.


19.7 OpenIntro Statistics

David Diez, Mine Cetinkaya-Rundel, Christopher Barr, and OpenIntro.

A complete foundation for Statistics, also serving as a foundation for Data Science.

Leanpub revenue supports OpenIntro (US-based nonprofit) so we can provide free desk copies to teachers interested in using OpenIntro Statistics in the classroom and expand the project to support free textbooks in other subjects.

More resources: openintro.org.

Pay what you want for the ebook, minimum $0.00, however if you are able to, please consider the cause above. Thanks!


19.8 Statistical inference for data science

Brian Caffo

This book gives a brief, but rigorous, treatment of statistical inference intended for practicing Data Scientists.

Pay what you want for the ebook, minimum $0.00


19.9 Statistics (The Easier Way) With R, 3rd. Ed. (TIDYVERSION)

Nicole Radziwill

This introductory applied statistics handbook shows you how to run tests analytically, and then how to run exactly the same steps using R. No steps are skipped, making this particularly well suited for beginners or people who need a quick lookup. Used at 30+ universities around the globe.

https://amzn.to/3b9ha8s - varies between $37-43 & you can request free PDF after your order https://www.e-junkie.com/ecom/gb.php?&c=single&cl=147256&i=1614407 - $25 for PDF only

19.10 End-to-End Solved Problems With R: a catalog of 26 examples using statistical inference

Nicole Radziwill

Lots of worked problems, analytically and in R! Useful supplement for an introductory applied stats class.

https://amzn.to/2EREAn2 - used for $4-18, new $19-20 https://www.e-junkie.com/ecom/gb.php?c=single&cl=147256&i=1548704 - $10 for PDF only

19.11 Bayes rules!

The primary goal of Bayes Rules! is to make modern Bayesian thinking, modeling, and computing accessible to a broad audience. Bayes Rules! empowers readers to weave Bayesian approaches into an everyday modern practice of statistics and data science.
The overall spirit is very applied: the book utilizes modern computing resources and a reproducible pipeline; the discussion emphasizes conceptual understanding; the material is motivated by data-driven inquiry; and the delivery blends traditional “content” with “activity”.

Free online book under construction but with 5 complete chapters on 2020/10/15


19.12 A Business Analyst’s Introduction to Business Analytics: Intro to Bayesian Business Analytics in the R Ecosystem

Adam Fleischhacker

This textbook goes farther than just showing you how to make computational models using software or mathematical models using statistics. It guides your thinking so you can align computational and mathematical models with real-world scenarios. As you journey through the material, you will feel empowered to effectively collaborate with business stakeholders as you use modern software stacks and modern statistical workflows to discover insight. R, RStudio, dplyr for data manipulation, ggplot for data visualization, causact for graphical models, and Bayesian data analysis feature prominently.

The full-color book ($68) is available via Amazon: https://www.amazon.com/dp/B08DBYPRD2 and online (free) at: http://causact.com. Video supplements for all chapters available on YouTube.

19.13 Statistics and Data with R: An Applied Approach Through Examples

Yosef Cohen and Jeremiah Y. Cohen

R, an Open Source software, has become the de facto statistical computing environment. It has an excellent collection of data manipulation and graphics capabilities. It is extensible and comes with a large number of packages that allow statistical analysis at all levels – from simple to advanced – and in numerous fields including Medicine, Genetics, Biology, Environmental Sciences, Geology, Social Sciences and much more. The software is maintained and developed by academicians and professionals and as such, is continuously evolving and up to date. Statistics and Data with R presents an accessible guide to data manipulations, statistical analysis and graphics using R.

The E-Book costs $97.00 while the print version costs $121.75