Open Educational Resources (OER)

The mission of OpenIntro is to make educational products that are free, transparent, and lower barriers to education. OpenIntro Statistics includes a free textbook, labs, videos, and additional resources.

Introductory Statistics follows scope and sequence requirements of a one-semester introduction to statistics course and is geared toward students majoring in fields other than math or engineering. The text assumes some knowledge of intermediate algebra and focuses on statistics application over theory.

This is a series of lesson plans based upon the OpenStax Introductory Statistics Textbooks. It contains sections on Sampling and Data, Descriptive Statistics, Probability, Discrete Random Variables, Continuous Random Variables, Distributions, The Central Limit Theorem, Confidence Intervals, Hypothesis Testing, Linear Regression and Correlation.

The goal of this tool is to make statistics more accessible through interactive visualizations. The authors of the site are also currently producing a textbook. These resources are free to access online but not open.

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. All of the materials are free and copiable, with source code maintained in Github.

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.

"This book starts by presenting an overview of the statistical thought process. By the end of chapter 2, students are already familiar with concepts such as hypotheses, level of significance, p-values, errors. Normally these topics are not introduced until after a discussion of probability and sampling distributions."

We organized this course around the Big Picture of Statistics. As we learn new material, we will always look at how these new ideas relate to the Big Picture.

Think Stats is an introduction to Probability and Statistics for Python programmers. Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets.