Math and Stats
OpenIntro (CC BY-SA)
OpenIntro provides three textbooks (OpenIntro Statistics, Introductory Statistics with Randomization and Simulation, and Advanced High School Statistics) along with a collection of ancillary resources including videos, labs, lecture slides, sample exams, and syllabuses.
Statistics LibreTexts Library (CC BY-NC-SA)
A collection of open textbooks, assignments, and other educational resources on sujects related to statistics.
Principles of Quality Assurance (CC BY)
Hybrid/blended course that introduces the scope and function of quality assurance, including basic definitions, statistics, quality policy and objectives, manuals and procedures, concept of variation, inspection and sampling techniques, metrology process control, methods and elements of reliability. Current (TQM) and ISO 9000 standards are reviewed.
Probability & Statistics (CC BY-NC-SA)
This course introduces students to the basic concepts and logic of statistical reasoning and gives the students introductory-level practical ability to choose, generate, and properly interpret appropriate descriptive and inferential methods. In addition, the course helps students gain an appreciation for the diverse applications of statistics and its relevance to their lives and fields of study. The course does not assume any prior knowledge in statistics and its only prerequisite is basic algebra.
Statistical Reasoning (CC BY-NC-SA)
Probability & Statistics introduces students to the basic concepts and logic of statistical reasoning and gives the students introductory-level practical ability to choose, generate, and properly interpret appropriate descriptive and inferential methods. In addition, the course helps students gain an appreciation for the diverse applications of statistics and its relevance to their lives and fields of study. The course does not assume any prior knowledge in statistics and its only prerequisite is basic algebra.
Grasple (Various CC licences)
Curated open exercises and lessons on math and stats created by the community.
B.C. Open Textbook Collection: Math/Statistics (Various CC licences)
A collection of open textbooks on various topics relating to math and statistics.
Beginning Statistics (CC BY-SA)
This book is meant to be a textbook for a standard one-semester introductory statistics course for general education students.
Engineering Statistics Handbook (Public domain)
The NIST/SEMATECH e-Handbook of Statistical Methods is a web-based book whose goal is to help scientists and engineers incorporate statistical methods into their work as efficiently as possible. Ideally it will serve as a reference that will help scientists and engineers design their own experiments and carry out the appropriate analyses when a statistician is not available to help. It is also hoped that it will serve as a useful educational tool that will help users of statistical methods and consumers of statistical information better understand statistical procedures and their underlying assumptions and more clearly interpret scientific and engineering results stated in statistical terms. The book is available online and as a PDF.
Introduction to Probability (GNU Free Documentation Licence)
This introductory probability book, published by the American Mathematical Society, emphasizes the use of computing to simulate experiments and make computations. The authors have prepared a set of programs to go with the book, along with solutions available to instructors. They also provide links to other probability resources.
This book of learning statistics with jamovi covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students. Descriptive statistics and graphing are followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. The book covers the analysis of contingency tables, correlation, t-tests, regression, ANOVA and factor analysis.
This book 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.
This textbook is intended for a one-semester, undergraduate statistics course. There are many concrete, specific questions that humans have about the world which are best answered by carefully collecting some data and using a modest amount of mathematics and a fair bit of logic to analyze them. It is, therefore, the goal of this book to help you learn How to Tell the Truth with Statistics and, therefore, how to tell when others are telling the truth … or are faking their “news.”
Online Statistics: An Interactive Multimedia Course of Study is a resource for learning and teaching introductory statistics. It contains material presented in textbook format and as video presentations. This resource features interactive demonstrations and simulations, case studies, and an analysis lab. It also includes resources for instructors, such as an instructor’s manual, PowerPoint slides, and additional questions.
OpenIntro Statistics – 4th ed (CC BY-SA)
This book offers a traditional introduction to statistics at the college level published on OpenIntro.
Probability and Statistics EBook (CC BY)
A general statistics curriculum E-Book, which includes Advanced-Placement (AP) materials.
Random is a website devoted to probability, mathematical statistics, and stochastic processes, and is intended for teachers and students of these subjects. The site consists of an integrated set of components that includes expository text, interactive web apps, data sets, biographical sketches, and an object library. Please read the Introduction for more information about the content, structure, mathematical prerequisites, technologies, and organization of the project.
R for Data Science (CC BY-NC-ND)
This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.
Statistical Methods in Quality Control [PDF] (CC BY-SA)
This book is the outcome of more than 30 years teaching a course titled “Statistical Quality Control” to B.S. students. The book is divided into fourteen chapters that cover the topics on statistical quality control needed for a one-semester course. Due to the importance of control charts and Acceptance Sampling Standards, most chapters of the book deal with the control charts and Standard Sampling Tables.
Statistical Thinking for the 21st Century (CC BY-NC)
A stats books designed for psychology students that teaches the approaches that are increasingly used in real statistical practice in the 21st century.
Models, Assumptions and Confidence Limits (CC BY-NC-ND)
Confidence intervals reflect our uncertainty about a parameter of interest, and models reflect our assumptions about the context of the data. Some of these assumptions may be justified by background knowledge, but others will be rather arbitrary. Statistics textbooks advise that before assuming a model we should check that it gives a good fit to the data (by using goodness-of-fit tests or graphical diagnostics). But does a well-fitting model necessarily mean a good confidence interval? Looking at the robustness of confidence limits to model choice suggests some rather basic questions about our use of models and assumptions in statistics.