This textbook is part of the OpenIntro Statistics series and offers complete coverage of the high school AP Statistics curriculum. Real data and plenty of inline examples and exercises make this an engaging and readable book. Links to lecture slides, video overviews, calculator tutorials, and video solutions to selected end of chapter exercises make this an ideal choice for any high school or Community College teacher. In fact, Portland Community College recently adopted this textbook for its Introductory Statistics course, and it estimates that this will save their students $250,000 per year. Find out more at: openintro.org/ahss

View our video tutorials here:
openintro.org/casio
openintro.org/TI

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 repositories.

This is a "first course" in the sense that it presumes no previous course in probability. The units are modules taken from the unpublished text: Paul E. Pfeiffer, ELEMENTS OF APPLIED PROBABILITY, USING MATLAB. The units are numbered as they appear in the text, although of course they may be used in any desired order. For those who wish to use the order of the text, an outline is provided, with indication of which modules contain the material.

This course is an arithmetic course intended for college students, covering whole numbers, fractions, decimals, percents, ratios and proportions, geometry, measurement, statistics, and integers using an integrated geometry and statistics approach. The course uses the late integers model—integers are only introduced at the end of the course.

Published by OpenStax College, Collaborative Statistics was written by Barbara Illowsky and Susan Dean, faculty members at De Anza College in Cupertino, California. The textbook was developed over several years and has been used in regular and honors-level classroom settings and in distance learning classes. This textbook is intended for introductory statistics courses being taken by students at two– and four–year colleges who are majoring in fields other than math or engineering. Intermediate algebra is the only prerequisite. The book focuses on applications of statistical knowledge rather than the theory behind it.

Overview: Table of Contents
Chapter 1: Fundamentals of Geometry
Chapter 2 – Measurement and Dimensional Analysis
Chapter 3: Linear Functions
Chapter 4: Percentages
Chapter 5: Savings
Chapter 6: Annuities and Loans
Chapter 7: Sets and Venn Diagrams
Chapter 8 – Introduction to Probability
Chapter 9 – Probability and Counting
Chapter 10 - Statistics
Chapter 11 – Describing Data
Chapter 12 – The Normal Distribution

This text was written for a college-level liberal arts math class. It addresses math concepts that most people will encounter as part of their everyday life, including statistics, probability, growth, finance, graph theory, voting systems, fair division, apportionment, and geometric symmetry, and the golden ratio. Each chapter includes a set of homework questions for practice.

This book discusses what is currently known about software engineering, based on an analysis of all the publicly available data. This aim is not as ambitious as it sounds, because there is not a great deal of data publicly available.

The intent is to provide material that is useful to professional developers working in industry; until recently researchers in software engineering have been more interested in vanity work, promoted by ego and bluster.

The material is organized in two parts, the first covering software engineering and the second the statistics likely to be needed for the analysis of software engineering data.

Midterm Exam Review 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.

Practice Final Exam 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.

Junior Lab consists of two undergraduate courses in experimental physics. The courses are offered by the MIT Physics Department, and are usually taken by Juniors (hence the name). Officially, the courses are called Experimental Physics I and II and are numbered 8.13 for the first half, given in the fall semester, and 8.14 for the second half, given in the spring.The purposes of Junior Lab are to give students hands-on experience with some of the experimental basis of modern physics and, in the process, to deepen their understanding of the relations between experiment and theory, mostly in atomic and nuclear physics. Each term, students choose 5 different experiments from a list of 21 total labs.

Homework 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.

Homework 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.

Homework 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.

Homework 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.

Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. This text covers more advanced graphical summaries, One-Way ANOVA with pair-wise comparisons, Two-Way ANOVA, Chi-square testing, and simple and multiple linear regression models. Models with interactions are discussed in the Two-Way ANOVA and multiple linear regression setting with categorical explanatory variables. Randomization-based inferences are used to introduce new parametric distributions and to enhance understanding of what evidence against the null hypothesis “looks like”. Throughout, the use of the statistical software R via Rstudio is emphasized with all useful code and data sets provided within the text. This is Version 3.0 of the book.

We are constantly bombarded by information, and finding a way to filter that information in an objective way is crucial to surviving this onslaught with your sanity intact. This is what statistics, and logic we use in it, enables us to do. Through the lens of statistics, we learn to find the signal hidden in the noise when it is there and to know when an apparent trend or pattern is really just randomness. The study of statistics involves math and relies upon calculations of numbers. But it also relies heavily on how the numbers are chosen and how the statistics are interpreted.

This work was created as part of the University of Missouri’s Affordable and Open Access Educational Resources Initiative (https://www.umsystem.edu/ums/aa/oer). The contents of this work have been adapted from the following Open Access Resources: Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University. Changes to the original works were made by Dr. Garett C. Foster in the Department of Psychological Sciences to tailor the text to fit the needs of the introductory statistics course for psychology majors at the University of Missouri – St. Louis. Materials from the original sources have been combined, reorganized, and added to by the current author, and any conceptual, mathematical, or typographical errors are the responsibility of the current author.

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.