On this page you will find several open Statistics textbooks along with supplemental material and a few lecture videos.
The purpose of these discipline specific pages is to showcase content that might be of interest to faculty who are considering adopting open educational resources for use in their classes. This list of content is by no means exhaustive. The nature of open educational resources is very collaborative and it is in that spirit that we encourage any comments about the content featured on this page or recommendations of content that are not already listed here.
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Introductory Statistics follows the scope and sequence of a onesemester, introduction to statistics course and is geared toward students majoring in fields other than math or engineering. This text assumes students have been exposed to intermediate algebra, and it focuses on the applications of statistical knowledge rather than the theory behind it. The foundation of this textbook is Collaborative Statistics, by Barbara Illowsky and Susan Dean, which has been widely adopted. Introductory Statistics includes innovations in art, terminology, and practical applications, all with a goal of increasing relevance and accessibility for students. We strove to make the discipline meaningful and memorable, so that students can draw a working knowledge from it that will enrich their future studies and help them make sense of the world around them. The text also includes Collaborative Exercises, integration with TI83,83+,84+ Calculators, technology integration problems, and statistics labs. OpenStax Senior Contributors: Barbara Illowsky, De Anza College This work is licensed under a Creative Commons Attribution 3.0 Unported License. 

The authors of this text intend for the reader to develop a foundational understanding of statistical thinking methods. Statistics is an applied field with a wide range of practical applications which a student does not have to be a math expert to understand even when using real, interesting data. Emphasized in this text is the practical applications of statistical tools. The authors have highlighted their imperfections and how student can use them to learn about the real world. OpenIntro. This textbook has been adopted by OU faculty member, Dr. Claude Miller. Authors: David M. Diez, Google/YouTube, Quantitative Analyst Christopher D. Barr, Harvard School of Public Health, Biostatistics Mine ÇetinkayaRundel, Duke University, Statistics This text is licensed under a Creative Commons AttributionNonCommercialShareAlike license. 

Combinatorics Through Guided Discovery Open Textbook Library This book is an introduction to combinatorial mathematics, also known as combinatorics. The book focuses especially but not exclusively on the part of combinatorics that mathematicians refer to as “counting.” The book consists almost entirely of problems. Some of the problems are designed to lead you to think about a concept, others are designed to help you figure out a concept and state a theorem about it, while still others ask you to prove the theorem. Other problems give you a chance to use a theorem you have proved. From time to time there is a discussion that pulls together some of the things you have learned or introduces a new idea for you to work with. Many of the problems are designed to build up your intuition for how combinatorial mathematics works. Open Textbook Library Author: Kenneth Bogart, Dartmouth College, Mathematics This text is licensed under a GNU Free Documentation License. 

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. David Lane Lead Developer: David Lane, Rice University, Statistics This text is in the Public Domain.


Think Bayes: Bayesian Statistics in Python Allen B. Downey Think Bayes is an introduction to Bayesian statistics using computational methods. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops. Allen B. Downey Author: Allen B. Downey, Ph.D., Computer Science, Olin College This text is licensed under a Creative Commons AttributionNonCommercial 3.0 License. 

Think Stats: Probability and Statistics for Programmers Allen B. Downey 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. if you have basic skills in Python, you can use them to learn concepts in probability and statistics. Think Stats is based on a Python library for probability distributions (PMFs and CDFs). Many of the exercises use short programs to run experiments and help readers develop understanding. Allen B. Downey
Author:
Allen B. Downey, Ph.D., Computer Science, Olin College
This text is licensed under a Creative Commons AttributionNonCommercial 3.0 License.

FlowingData explores how designers, statisticians, and computer scientists are using data to understand ourselves better — mainly through data visualization. Nathan Yau Developer: Nathan Yau, Ph. D., University of California, Los Angeles, Statistics Unless otherwise noted, graphics and text on this site are licensed under a Creative Commons AttributionNonCommercial License. Original authors should be contacted regarding their work. 

Probability and Statistics Videos Khan Academy Khan Academy features a collection of tutorial videos on the subject of Probability and Statistics. This collection features multiple videos on each of the following topics: independent and dependent events, probability and combinatorics, descriptive statistics, random variables and probability distributions, regression, and inferential statistics. This work is licensed under a Creative Commons AttributionNoncommercialShare Alike 3.0 United States License. 

Statistical Reasoning Carnegie Mellon University Statistical Reasoning introduces students to the basic concepts and logic of statistical reasoning and gives the students introductorylevel 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. Carnegie Mellon University This site is licensed under a Creative Commons AttributionNonCommercialShareAlike License. 
Probabilistic Systems Analysis and Applied Probability MIT OpenCourseware This course focuses on the modeling and analysis of random phenomena and processes, including the basics of statistical inference. Nowadays, there is broad consensus that the ability to think probabilistically is a fundamental component of scientific literacy. MIT Open CourseWare Instructor: Prof. John Tsitsiklis, Massachusetts Institute of Technology, Electrical Engineering Use of the MIT OpenCourseWare site and materials is subject to their Creative Commons License and other terms of use. 

Statistics for Behavorial Science New York University This course provides students with the basic tools for evaluating data from studies in the behavioral sciences, particularly psychology. Students will gain familiarity with data description, variance and variability, significance tests, confident intervals, correlation and linear regression, analysis of variance, and other related topics. The goal is to learn the application of statistical reasoning to decision making. Current events are often used to illustrate these issues. New York University Instructor: Professor Elizabeth Bauer, New York University, Arts and Sciences The content in this course is licensed under a Creative Commons AttributionNonCommercialShareAlike license. 
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