This course blends Introductory Statistics from OpenStax with other OER to offer a first course in statistics intended for students majoring in fields other than mathematics and engineering. This course 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 the OpenStax text is Collaborative Statistics, by Barbara Illowsky and Susan Dean. The development choices for this textbook were made with the guidance of many faculty members who are deeply involved in teaching this course. These choices led to 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, so that students can draw from it a working knowledge that will enrich their future studies and help them make sense of the world around them.

This open course for Elementary Statistics was created through a Round Ten Textbook Transformation Grant:

https://oer.galileo.usg.edu/mathematics-collections/39/

The open course contains ancillary materials for OpenStax Introductory Statistics:

https://openstax.org/details/books/introductory-statistics

Included in the course are introductions to each lesson, lecture slides, videos, and problem questions. Topics include:

Types of Data
Sampling Techniques
Qualitative Data
Frequency Distributions
Descriptive Statistics
Variation and Position
Confidence Intervals
Hypothesis Testing
Chi-Square Goodness of Fit
Linear Regression
Variance ANOVA

This google drive has notes for an Elementary Statistics I course. The chapter and sections numbers correspond to the OER textbook by Openstax, Introductory Statistics.

This google drive has notes for an Elementary Statistics II course. The chapter and section numbers correspond to the OER textbook by Openstax, Introductory Statistics.

This resource contains class notes and exercises for a course on Fundamental Statistics. Each section is provided in its own Google Doc to allow for remixing and adapting as needed.

The book aims to provide a more holistic approach to supplement traditional statistics textbooks. It includes deriving hypotheses from research questions, explaining statistical power and Type II error more in-depth, emphasizing conceptual understanding over calculations, using software for computations, adding interactive animations, and providing Excel data sets. Preliminary student results showed increased engagement and comprehension of hypothesis testing logic after incorporating a holistic approach.

The book is published under a Creative Commons license to make it freely available to students, as the author believes in open sharing of educational resources. The introduction references stories about drug research failures to illustrate Type I and Type II errors and includes a poem about blind men feeling different elephant parts to represent flaws in reductionist-only research. Overall, the book seeks to bring a more holistic perspective to the traditionally reductionist inferential statistics curriculum.

The target audience for this book is college students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more. It is assumed that the students do have basic skills in using computers and have access to one. Moreover, it is assumed that the students are willing to actively follow the discussion in the text, to practice, and more importantly, to think.

Introduction to Statistics is a resource for learning and teaching introductory statistics. This work is in the public domain. Therefore, it can be copied and reproduced without limitation. However, we would appreciate a citation where possible. Please cite as: Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University. Instructor's manual, PowerPoint Slides, and additional questions are available.

The main goal of the course is to highlight the general assumptions and methods that underlie all statistical analysis. The purpose is to get a good understanding of the scope, and the limitations of these methods. We also want to learn as much as possible about the assumptions behind the most common methods, in order to evaluate if they apply with reasonable accuracy to a given situation. Our goal is not so much learning bread and butter techniques: these are pre-programmed in widely available and used software, so much so that a mechanical acquisition of these techniques could be quickly done "on the job". What is more challenging is the evaluation of what the results of a statistical procedure really mean, how reliable they are in given circumstances, and what their limitations are.Login: guest_oclPassword: ocl

Susan Dean and Barbara Illowsky (Published 2013 by OpenStax College)
Adapted by: College of Lake County Faculty: Riyanti Boyd and Natalia Casper
Revised July 2021

This project was funded by a grant from the College of Lake County Foundation.
Adapted College of Lake County text can be found at:
http://dept.clcillinois.edu/mth/oer/IntroductoryStatistics.pdf

Original text materials for Introductory Statistics by Dean and Illowski 2013
available at: http://cnx.org/content/col11562/latest/.

Original Publication Under the following license:

This work is licensed under the
Creative Commons Attribution-Noncommercial-Share Alike 3.0 unported license.
To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/
or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco,
California, 94105, USA.

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. Introductory Statistics includes innovative practical applications that make the text relevant and accessible, as well as collaborative exercises, technology integration problems, and statistics labs.

We hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods.

(1) Statistics is an applied field with a wide range of practical applications.

(2) You don't have to be a math guru to learn from interesting, real data.

(3) 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.

"Rising textbook costs are a potential barrier to student progression and retention. Therefore, a group of faculty in the department of Mathematics at Augusta University has decided to adopt an open textbook for use in an Elementary Statistics course.

We have developed a set of materials to accompany the OpenStax Introductory Statistics textbook by Illowsky and Dean including lecture/study notes and projects.

These materials are meant as a supplement for teachers and learners who are using the OpenStax Introductory Statistics textbook by Illowsky and Dean. These materials also incorporate the use of other technologies (R and Excel) in an open statistics course for students and instructors who wish to use technologies other than the TI-83/84 graphing calcuator."

This is a first draft of a free (as in speech, not as in beer, [Sta02]) (although it is free as in beer as well) textbook for a one-semester, undergraduate statistics course. It was used for Math 156 at Colorado State University–Pueblo in the spring semester of 2017.

This text is for an introductory level probability and statistics course with an intermediate algebra prerequisite. The focus of the text follows the American Statistical Association’s Guidelines for Assessment and Instruction in Statistics Education (GAISE). Software examples provided for Microsoft Excel, TI-84 & TI-89 calculators. A separate document is provided on the website with examples in
SPSS.

Students new to statistics are sure to benefit from this fully ADA accessible and relevant textbook. The examples resonate with everyday life, the text is approachable, and has a conversational tone to provide an inclusive and easy to read format for students.

This textbook incorporates the compilation of my notes over the years teaching introductory probability and statistics courses used to expand and enhance upon the open educational resource Statistics Using Technology by Kathryn Kozak, with some additional material adapted from OpenIntro Statistics by Diez,
Barr, and Çetinkaya-Rundel. Both texts are licensed under CC BY-SA 4.0. This textbook, except for the cover art, is licensed under a Creative Commons Attribution-Share Alike 4.0 International License (CCBY-SA 4.0).

Review and content editing by Jennifer Ward. Recommended definition edits and glossary terms by Dr. Naghmeh Daneshi. Alternative text for images provided by Whitney Cave. Additional exercises from Whitney Cave and from MyOpenMath: https://www.myopenmath.com/. Copy editing by Walter Benson. Cover art by James Tadlock https://www.artstation.com/redhedron.

For instructors, there is a complete solution manual and two companion online homework courses. An adaptive learning system called Realizeit which has content from the text, videos and homework problems. A free MyOpenMath course with problems divided by chapter. Email Rachel Webb for more information.

OpenIntro Statistics strives to be a complete introductory textbook of the highest caliber. Its core derives from the classic notions of statistics education and is extended by recent innovations. The textbook meets high quality standards and has been used at Princeton, Vanderbilt, UMass Amherst, and many other schools. We look forward to expanding the reach of the project and working with teachers from all colleges and schools.

This is a new approach to an introductory statistical inference textbook, motivated by probability theory as logic. It is targeted to the typical Statistics 101 college student, and covers the topics typically covered in the first semester of such a course. It is freely available under the Creative Commons License, and includes a software library in Python for making some of the calculations and visualizations easier.

This is a list of terms and their definitions related to statistics provided by the Australian Bureau of Statistics, an organ of the government of the Commonwealth of Australia.

Statistical thinking is a way of understanding a complex world by describing it in relatively simple terms that nonetheless capture essential aspects of its structure, and that also provide us some idea of how uncertain we are about our knowledge. The foundations of statistical thinking come primarily from mathematics and statistics, but also from computer science, psychology, and other fields of study.

Introductory statistics course developed through the Ohio Department of Higher Education OER Innovation Grant. The course is part of the Ohio Transfer Module and is also named TMM010. For more information about credit transfer between Ohio colleges and universities please visit: www.ohiohighered.org/transfer.