Introductory Business Statistics is designed to meet the scope and sequence requirements of the one-semester statistics course for business, economics, and related majors. Core statistical concepts and skills have been augmented with practical business examples, scenarios, and exercises. The result is a meaningful understanding of the discipline, which will serve students in their business careers and real-world experiences.

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. 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.

Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

Lecture for the course "CS 217 – Probability and Statistics for Computer Science" delivered at the City College of New York in Spring 2019 by Evan Agovino as part of the Tech-in-Residence Corps program.

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.

Math in Society is a free, open textbook. This book is a survey of contemporary mathematical topics, most non-algebraic, appropriate for a college-level quantitative literacy topics course for liberal arts majors. The text is designed so that most chapters are independent, allowing the instructor to choose a selection of topics to be covered. Emphasis is placed on the applicability of mathematics. Core material for each topic is covered in the main text, with additional depth available through exploration exercises appropriate for in-class, group, or individual investigation.

Math in Society is a free, open textbook. This book is a survey of contemporary mathematical topics, most non-algebraic, appropriate for a college-level quantitative literacy topics course for liberal arts majors. The text is designed so that most chapters are independent, allowing the instructor to choose a selection of topics to be covered. Emphasis is placed on the applicability of the mathematics. Core material for each topic is covered in the main text, with additional depth available through exploration exercises appropriate for in-class, group, or individual investigation. This book is appropriate for Washington State Community Colleges' Math 107.

OpenIntro Statistics offers a traditional introduction to statistics at the college level. This textbook is widely used at the college level and offers an exceptional and accessible introduction for students from community colleges to the Ivy League. The textbook has been thoroughly vetted with an estimated 20,000 students using it annually.

Risk analysis, assessment, and management is essential to any engineering field. This course is designed to provide students with an understanding of how to perform a comprehensive risk assessment applicable to a wide variety of engineering problems. The course will focus on failure mode and effect analysis, fault tree analysis, probabilistic risk analysis, and human reliability analysis. The course will also cover fundamental probability and statistics content.

A student's guide to performing frequentist statistical analyses using JASP. This document is a collection of standalone handouts covering the most common standard (frequentist) statistical analyses used by students studying Biological Sciences. Datasets used in this document are available for download from: https://osf.io/bx6uv/.

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.