22.1 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!
22.2 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
22.3 A Business Analyst’s Introduction to Business Analytics: Intro to Bayesian Business Analytics in the R Ecosystem
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.
22.4 Common statistical tests are linear models: a work through
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.
22.5 Foundations of Statistics with R
Darrin Speegle and Bryan Clair
This book represents a fundamental rethinking of a calculus based first course in probability and statistics. We offer a breadth first approach, where the fundamentals of probability and statistics can be taught in one semester. The statistical programming language R plays an essential role throughout the text through simulations, data wrangling, visualizations and statistical procedures. Data sets from a variety of sources, including many from recent, open source scientific articles, are used in examples and exercises. Demonstrations of important facts are given through simulations, with some formal mathematical proofs as well.
This book is an excellent choice for students studying data science, statistics, engineering, computer science, mathematics, science, business, or any field which requires the two semesters of calculus needed to read this book.
22.6 Handbook of Regression Modeling in People Analytics
It is the author’s firm belief that all people analytics professionals should have a strong understanding of regression models and how to implement and interpret them in practice, and the aim with this book is to provide those who need it with help in getting there.
22.7 Learning statistics with R: A tutorial for psychology students and other beginners. (Version 0.6.1)
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.
22.8 Mixed Models with R : Getting started with random effects
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.
22.9 An Introduction to Statistical and Data Sciences via R
An incredibly beginner friendly introduction to both datascience and statistics concepts as well as R.
The book is free to read online.
22.10 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.
22.11 Statistical Rethinking with brms, ggplot2, and the tidyverse
A Solomon Kurz
This is a love letter I love McElreath’s Statistical Rethinking text. It’s the entry-level textbook for applied researchers I spent years looking for. McElreath’s freely-available lectures on the book are really great, too.
However, I prefer using Bürkner’s brms package when doing Bayeian regression in R. It’s just spectacular. I also prefer plotting with Wickham’s ggplot2, and coding with functions and principles from the tidyverse
So, this project is an attempt to reexpress the code in McElreath’s textbook. His models are re-fit with brms, the figures are reproduced or reimagined with ggplot2, and the general data wrangling code now predominantly follows the tidyverse style.
22.12 OpenIntro Statistics
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!
22.13 Introduction to Modern Statistics
We hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods.
- Statistics is an applied field with a wide range of practical applications.
- You don’t have to be a math guru to learn from interesting, real data.
- Data are messy, and statistical tools are imperfect. However, when you understand the strengths and weaknesses of these tools, you can use them to learn interesting things about the~world.
22.14 Statistical inference for data science
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
22.15 Statistics (The Easier Way) With R, 3rd. Ed. (TIDYVERSION)
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
22.16 End-to-End Solved Problems With R: a catalog of 26 examples using statistical inference
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
22.17 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
22.18 TEACUPS, GIRAFFES, & STATISTICS
HASSE WALUM, DESIRÉE DE LEON
A delightful series of beautifully illustrated modules to learn statistics and R coding for students, scientists, and stats-enthusiasts.
22.19 Modern Statistics with R
This book covers the fundamentals of data science and statistics. The first half deals with the basics of R and R coding, data wrangling, exploratory data analysis and more advandced programming. The second half deals with modern statistics (favouring permutation tests, the bootstrap and Bayesian methods over traditional asymptotic methods), regression models and predictive modelling. It also contains information about debugging and explanations of 25 commonly encountered error messages in R. In addition, there are 170 or so exercises with fully worked solutions.
##Foundations of Statistics with R Darrin Speegle
This book represents a fundamental rethinking of a calculus based first course in probability and statistics. We offer a breadth first approach, where the fundamentals of probability and statistics can be taught in one semester.1 The statistical programming language R plays an essential role throughout the text through simulations, data wrangling, visualizations and statistical procedures. Data sets from a variety of sources, including many from recent, open source scientific articles, are used in examples and exercises. Demonstrations of important facts are given through simulations, with some formal mathematical proofs as well.