27.1 A Business Analyst’s Introduction to Business Analytics
This textbook goes farther than just teaching you to make computational models using software or mathematical models using statistics. It teaches you how to align computational and mathematical models with real-world scenarios; empowering you to communicate with and leverage the expertise of business stakeholders while using modern software stacks and statistical workflows. In this book, you do not learn business analytics to make models; you learn business analytics to add tangible value in the real-world.
27.2 An Introduction to Statistical and Data Sciences via R
An incredibly beginner friendly introduction to both datascience and statistics concepts as well as R.
27.3 An Introduction to Statistical Learning
by Gareth James, Daniela Witten, Trevor Hastie, Rob Tibshirani
As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. Each chapter includes an R lab. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.
27.4 Answering questions with data
by 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.
(Oscar’s note:Looks like a comprehensive stats resource!)
27.5 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”.
27.6 Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R
by Paul Roback, Julie Legler
This book is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling.
27.7 Common statistical tests are linear models a work through
by 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.
27.8 Doing meta-analysis with R A hands-on guide
by Mathias Harrer, Pim Cuijpers, Toshi A. Furukawa, David D. Ebert
This book serves as an accessible introduction into how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools.
Advanced, but highly relevant topics such as network meta-analysis, multi-/three-level meta-analyses, Bayesian meta-analysis approaches, SEM meta-analysis are also covered.
27.9 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
27.10 Foundations of Statistics with R
by Darrin Speegle, 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.
27.11 Foundations of Statistics with R
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.
27.12 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.
For accompanying solutions to some of the questions: https://keithmcnulty.github.io/peopleanalytics-regression-book/solutions/
27.13 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.
27.14 ISLR tidymodels Labs
This book aims to be a complement to the 1st version An Introduction to Statistical Learning book with translations of the labs into using the tidymodels set of packages.
The labs will be mirrored quite closely to stay true to the original material.
27.15 Learning statistics with R A tutorial for psychology students and other beginners
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 <U+FB01>rst, 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.
27.16 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.
27.17 Model Estimation by Example Demonstrations with R
This document provides ‘by-hand’ demonstrations of various models and algorithms. The goal is to take away some of the mystery of them by providing clean code examples that are easy to run and compare with other tools.
The code was collected over several years, so is not exactly consistent in style, but now has been cleaned up to make it more so. Within each demo, you will generally find some imported/simulated data, a primary estimating function, a comparison of results with some R package, and a link to the old code that was the initial demonstration.
27.18 Modern Statistical Methods for Astronomy
Modern astronomical research is beset with a vast range of statistical challenges, ranging from reducing data from mega datasets to characterizing an amazing variety of variable celestial objects or testing astrophysical theory. Linking astronomy to the world of modern statistics, this volume is a unique resource, introducing astronomers to advanced statistics through ready-to-use code in the public-domain R statistical software environment. The book presents fundamental results of probability theory and statistical inference, before exploring several fields of applied statistics, such as data smoothing, regression, multivariate analysis and classification, treatment of non-detections, time series analysis, and spatial point processes. It applies the methods discussed to contemporary astronomical research datasets using the R statistical software, making it an invaluable resource for graduate students and researchers facing complex data analysis task.
27.19 Modern Statistics with R
by Måns Thulin
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.
27.20 One Way ANOVA with R Completely Randomized Design - Between Groups
by Bruce Dudek
This document can be a standalone “how-to” document for R users. However, it is primarily intended for students in the APSY510/511 statistics sequence at the University at Albany. It is a fairly thorough treatment of graphical and inferential evaluation of one-factor designs. It presumes prior background coverage of the ANOVA logic from standard textbooks such as Howell or Maxwell, Delaney and Kelley (2017). The analyses are intended to parallel and exhaust the methods already covered with SPSS, and to extend them to additional topics.
27.21 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.
Paid: Pay what you want for the ebook, minimum $0.00, however if you are able to, please consider the cause above. Thanks! $15
27.22 Statistical inference for data science
by Brian Caffo
This book gives a brief, but rigorous, treatment of statistical inference intended for practicing Data Scientists.
Paid: Free or pay what you want $15
27.23 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.
27.24 Statistical Rethinking with brms, ggplot2, and the tidyverse Second edition
by A Solomon Kurz
This ebook is based on the second edition of Richard McElreath’s (2020) text, Statistical rethinking: A Bayesian course with examples in R and Stan. My contributions show how to fit the models he covered with Paul Bürkner’s brms package, which makes it easy to fit Bayesian regression models in R using Hamiltonian Monte Carlo. I also prefer plotting and data wrangling with the packages from the tidyverse. So we’ll be using those methods, too.
27.25 Statistical Thinking in the 21st Century
This textbook aims to cover modern methods that take advantage of today’s increased computing power, while also balancing the accessibility of the material for students not wanting to wade through a lot of story to get to the statistical knowledge while reading Andy Field’s graphic novel statistics books, “An Adventure in Statistics”.
The main site below has companion sites in R and Python:
- R companion https://statsthinking21.github.io/statsthinking21-R-site/
- Python companion https://statsthinking21.github.io/statsthinking21-python/
27.26 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 https://www.e-junkie.com/ecom/gb.php?&c=single&cl=147256&i=1614407 - $25 for PDF only
27.27 Statistics and Data with R An Applied Approach Through Examples
by Yosef Cohen, 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.
Paid: The E-Book costs $97.00 while the print version costs $121.75 $97
27.28 Teacups, Giraffes and Statistics
A delightful series of beautifully illustrated modules to learn statistics and R coding for students, scientists, and stats-enthusiasts.
27.29 The Effect An Introduction to Research Design and Causality
The Effect is a book intended to introduce students (and non-students) to the concepts of research design and causality in the context of observational data. The book is written in an intuitive and approachable way and doesn’t overload on technical detail. Why teach regression and research design at the same time when they are fundamentally different things? First learn why you want to structure a design in a certain way, and what it is you want to do to the data, and then afterwards learn the technical details of how to run the appropriate model.
27.30 Using R for Bayesian Spatial and Spatio-Temporal Health Modeling
by Andrew B. Lawson
Progressively more and more attention has been paid to how location affects health outcomes. The area of disease mapping focusses on these problems, and the Bayesian paradigm has a major role to play in the understanding of the complex interplay of context and individual predisposition in such studies of disease. Using R for Bayesian Spatial and Spatio-Temporal Health Modeling provides a major resource for those interested in applying Bayesian methodology in small area health data studies.
Created and maintained by Oscar Baruffa
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