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 formula packet and pdf version of the text are available on the website http://mostlyharmlessstatistics.com. Students new to probability and 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.

able of Contents
Chapter 1 Introduction to Data
Chapter 2 Organizing Data
Chapter 3 Descriptive Statistics
Chapter 4 Probability
Chapter 5 Discrete Probability Distributions
Chapter 6 Continuous Probability Distributions
Chapter 7 Confidence Intervals for One Population
Chapter 8 Hypothesis Tests for One Population
Chapter 9 Hypothesis Tests & Confidence Intervals for Two Populations
Chapter 10 Chi-Square Tests
Chapter 11 Analysis of Variance
Chapter 12 Correlation and Regression
Chapter 12 Formulas
Chapter 12 Exercises
Chapter 13 Nonparametric Tests

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.

Table of Contents
1. Introduction
2. Graphing Distributions
3. Summarizing Distributions
4. Describing Bivariate Data
5. Probability
6. Research Design
7. Normal Distributions
8. Advanced Graphs
9. Sampling Distributions
10. Estimation
11. Logic of Hypothesis Testing
12. Testing Means
13. Power
14. Regression
15. Analysis of Variance
16. Transformations
17. Chi Square
18. Distribution-Free Tests
19. Effect Size
20. Case Studies
21. Glossary

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.

Table of Contents
1 Introduction to Probability
2 Applications of Probability
3 Random Sequences and Visualization
4 Introduction to Model Comparison
5 Applications of Model Comparison
6 Introduction to Parameter Estimation
7 Priors, Likelihoods, and Posteriors
8 Common Statistical Significance Tests
9 Applications of Parameter Estimation and Inference
10 Multi-parameter Models
11 Introduction to MCMC
12 Concluding Thoughts
Bibliography
Appendix A: Computational Analysis
Appendix B: Notation and Standards
Appendix C: Common Distributions and Their Properties
Appendix D: Tables

This is a "first course" in the sense that it presumes no previous course in probability. The mathematical prerequisites are ordinary calculus and the elements of matrix algebra. A few standard series and integrals are used, and double integrals are evaluated as iterated integrals. The reader who can evaluate simple integrals can learn quickly from the examples how to deal with the iterated integrals used in the theory of expectation and conditional expectation. Appendix B provides a convenient compendium of mathematical facts used frequently in this work. And the symbolic toolbox, implementing MAPLE, may be used to evaluate integrals, if desired.

In addition to an introduction to the essential features of basic probability in terms of a precise mathematical model, the work describes and employs user defined MATLAB procedures and functions (which we refer to as m-programs, or simply programs) to solve many important problems in basic probability. This should make the work useful as a stand-alone exposition as well as a supplement to any of several current textbooks.

Most of the programs developed here were written in earlier versions of MATLAB, but have been revised slightly to make them quite compatible with MATLAB 7. In a few cases, alternate implementations are available in the Statistics Toolbox, but are implemented here directly from the basic MATLAB program, so that students need only that program (and the symbolic mathematics toolbox, if they desire its aid in evaluating integrals).

Since machine methods require precise formulation of problems in appropriate mathematical form, it is necessary to provide some supplementary analytical material, principally the so-called minterm analysis. This material is not only important for computational purposes, but is also useful in displaying some of the structure of the relationships among events.

Table of Contents
1 Preface
2 Probability Systems
3 Minterm Analysis
4 Conditional Probability
5 Independence of Events
6 Conditional Independence
7 Random Variables and Probabilities
8 Distribution and Density Functions
9 Random Vectors and joint Distributions
10 Independent Classes of Random Variables
11 Functions of Random Variables
12 Mathematical Expectation
13 Variance, Covariance, Linear Regression
14 Transform Methods
15 Conditional Expectation, Regression
16 Random Selection
17 Conditional Independence, Given a Random Vector
18 Appendices

Table of Contents

1 Sampling and Data
2 Descriptive Statistics
3 Probability Topics
4 Discrete Random Variables
5 Continuous Random Variables
6 The Normal Distribution
7 The Central Limit Theorem
8 Confidence Intervals
9 Hypothesis Testing: Single Mean and Single Proportion
10 Hypothesis Testing: Two Means, Paired Data, Two Proportions
11 The Chi-Square Distribution
12 Linear Regression and Correlation
13 F Distribution and ANOVA
14 Appendix
15 Tables

Collaborative Statistics was written by Barbara Illowsky and Susan Dean, faculty members at De Anza Collegein Cupertino, California. The textbook was developed over several years and has been used in regularand honors-level classroom settings and in distance learning classes. Courses using this textbook have beenarticulated by the University of California for transfer of credit. The textbook contains full materials forcourse offerings, including expository text, examples, labs, homework, and projects. A Teacher's Guide iscurrently available in print form and on the Connexions site at and supplemental course materials including additional problem sets and video lectures are available. The on-line text for each of these collections collections willmeet the Section 508 standards for accessibility.

An on-line course based on the textbook was also developed by Illowsky and Dean. It has won an awardas the best on-line California community college course. The on-line course will be available at a later dateas a collection in Connexions, and each lesson in the on-line course will be linked to the on-line textbookchapter. The on-line course will include, in addition to expository text and examples, videos of courselectures in captioned and non-captioned format.

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.

This is a review of Probability and Statistics EBook: https://louis.oercommons.org/courses/ap-statistics-curriculum-2007 completed by Dr. Esperanza Zenon, River Parishes Community College.This rubric was developed by BCcampus. This work is licensed under a Creative Commons Attribution 3.0 Unported license.The rubric allows reviewers to evaluate OER textbooks using a consistent set of criteria. Reviewers are encouraged to remix this rubric and add their review content within this tool. If you remix this rubric for an evaluation, please add the title to the evaluated content and link to it from your review.

Syllabus, videos, and student responses from the adoption of OpenIntro's Biostatistics text. The text has its own accompanying videos and a mostly self-grading homework platform.Instructor resources are available: https://www.openintro.org/teachers/. These resources accompany the open textbook Introductory Statistics for the Life and Biomedical Sciences.

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.

Access also available here: https://openstax.org/details/books/introductory-statistics

Table of Contents
Sampling and Data
Descriptive Statistics
Probability Topics
Discrete Random Variables
Continuous Random Variables
The Normal Distribution
The Central Limit Theorem
Confidence Intervals
Hypothesis Testing with One Sample
Hypothesis Testing with Two Samples
The Chi-Square Distribution
Linear Regression and Correlation
F Distribution and One-Way ANOVA

PowerPoint Slides to accompany Chapter 4 of OpenStax Statistics textbook. Prepared by River Parishes Community College (Jared Eusea, Assistant Professor of Mathematics, and Ginny Bradley, Instructor of Mathematics) for OpenStax Statistics textbook under a Creative Commons Attribution-ShareAlike 4.0 International License. Date provided: July 2019.

PowerPoint Slides to accompany Chapter 1 of OpenStax Statistics textbook. Prepared by River Parishes Community College (Jared Eusea, Assistant Professor of Mathematics, and Ginny Bradley, Instructor of Mathematics) for OpenStax Statistics textbook under a Creative Commons Attribution-ShareAlike 4.0 International License. Date provided: July 2019.