OpenIntro Statistics is a dynamic take on the traditional curriculum, being successfully used at Community Colleges to the Ivy League
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OpenIntro Statistics is recommended for college courses and self-study.
Getting Started
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FREE -- OpenIntro Statistics PDF
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$25 -- B&W paperback
Available on Amazon and in select bookstores
$40 -- OpenIntro Statistics, color paperback
Color internal pages, while the B&W version is gray-scaled
FREE -- Book PDF Best for Screen Readers
More detailed table of contents, extra text to ease aid navigation (e.g. explicitly noting when an example starts and ends), and "alt text" for all images. Note that page numbers do *not* align with the original PDF, so please use section, figure, example, et al numbers for referencing and navigation. Please send feedback via the openintro.org/contact page.
Learning objectives
What we hope students will learn from these resources
Data sets
List of data sets and the option to download files
Where to find more data sets
An incredible list of data organized by Shonda Kuiper
Send feedback or report a typo
We appreciate feedback, both positive and negative
List of known textbook typos
Review textbook typos and clarifications
Translations + Other International Distribution
For those using a translated version, please send your warm wishes to the team behind these translations! We deeply appreciate their contributions to the community!
A Japanese translation has been created by a team of Japanese faculty! This translation is available below in both PDF (on Dr. Kunitomo's page) and as an affordable paperback (via the Japanese Statistical Association).
FREE -- Japanese translation of OpenIntro Statistics PDF
Translation by Naoto Kunitomo, Yasushi Yoshida, & Atsuyuki Kogure
Japanese translation, B&W paperback for ¥1980
Translated by Naoto Kunitomo, Yasushi Yoshida, & Atsuyuki Kogure
A Chinese translation is under development by Shiyao Wang and Xueqi Li! A recent draft of the progress is available below.
FREE -- Chinese translation of Ch 1-6 (PDF)
Translation by Shiyao Wang & Xueqi Li
Follow the Chinese translation updates on WeChat
Leads to a WeChat page
A Vietnamese translation is currently under development by a volunteer team led by Associate Professor Do Thi Thanh Toan. A recent draft of the progress is available below. If you notice any typos or errors that need correction, please feel free to contact our corresponding member, Mr. Thanh Hai Pham (email: thanh.ph.hmu@gmail.com). We will do our best to respond promptly.
The team members working on the Vietnamese translation are: Associate Professor Do Thi Thanh Toan; Dr. Le Xuan Hung; Dr. Dinh Thai Son; Dr. Luu Ngoc Minh; Mr. Nguyen Trung Kien (BSc); Mrs. Tran Cat Khanh (BSc); Mr. Vu Gia Huan (MD); Mr. Ngo Gia Huy (MD) (email: huygiango3001@gmail.com); and Mr. Thanh Hai Pham (MSc) - Corresponding member (email: thanh.ph.hmu@gmail.com).
FREE -- Vietnamese translation, Ch 1 (PDF)
Translation by a team led by Professor Do Thi Thanh Toan
FREE -- Vietnamese translation, Ch 2 (PDF)
Translation by a team led by Professor Do Thi Thanh Toan
FREE -- Vietnamese translation, Ch 3 (PDF)
Translation by a team led by Professor Do Thi Thanh Toan
FREE -- Vietnamese translation, Ch 4 (PDF)
Translation by a team led by Professor Do Thi Thanh Toan
FREE -- Vietnamese translation, Ch 5 (PDF)
Translation by a team led by Professor Do Thi Thanh Toan
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English B&W paperback on Amazon.co.jp
See also the Japanese translation option
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Hello, northern neighbor
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Hello, from across the Atlantic
Notion Press (India) -- B&W paperback
Price includes shipping cost
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Book is in English
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Book is in English
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Amazon.it -- B&W paperback
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Teachers: General Resources
Resources for teachers, some of which are restricted to Verified Teachers only. Slides, labs, and other resources may also be found in the corresponding chapter sections below.
Learn about Teacher Verification
Benefits, options to apply, and the verification process
Request a textbook desk copy (US only)
Available to Verified Teachers, click here to apply for access
OpenIntro Statistics exercise solutions
Available to Verified Teachers, click here to apply for access
Bookstore Ordering (bulk)
Wholesale purchase options
MyOpenMath: online course software
Free course software, OpenIntro course templates are available
MyOpenMath: setting up an OpenIntro course
Course templates exist for some OpenIntro books
OpenIntro Statistics, info on past editions
Content, prices, and availability details
Teachers page with additional resources
Some public resources, others restricted to Verified Teachers
Teachers: Sample Syllabi
Teachers: Sample Exams
Restricted to Verified Teachers only.
OpenIntro Statistics Exams, Set 1
Available to Verified Teachers, click here to apply for access
Openintro Statistics Exams, Set 2
Available to Verified Teachers, click here to apply for access
Multiple choice exam question bank (RExams)
Available to Verified Teachers, click here to apply for access
OpenIntro Statistics, Sample Exams (Adam Gilbert)
Available to Verified Teachers, click here to apply for access
ISLBS, Sample Midterm and Final Exams (Julie Vu)
Available to Verified Teachers, click here to apply for access
ISRS, Sample Midterms and Final Exam (Albert Kim)
Available to Verified Teachers, click here to apply for access
What is Statistics?
Companion Notebook
Chapter 1: Intro to Data
Introduction to Data: 5 videos Videos for each section
1.1 - Using stents to prevent strokes
Real case study with a surprising finding
1.2 - Data basics
Typical data structures and properties
1.3A - Data collection principles
Thoughtful data collection is critical to learning from data
1.3B - Sampling principles and strategies
Different ways to sample from a population
1.4 - Experiments
Basic principles of experimental design
Google Slides & LaTeX variants available Slides for each section
Slides 1 - Intro to data
LaTeX slides for full chapter on Github
Slides 1.1 - Intro to data, case study
Google Slides version, can export to Powerpoint
Slides 1.2 - Data Basics
Google Slides version, can export to Powerpoint
Slides 1.3 - Sampling principles and strategies
Google Slides version, can export to Powerpoint
Slides 1.4 - Experiments
Google Slides version, can export to Powerpoint
Lab - Intro to Statistical Software
Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, SAS, Stata
Chapter 2: Summarizing Data
Summarizing Data: 3 videos Videos for each section
2.1 - Examining numerical data
Mean, standard deviation, histograms, box plots, and more
2.2 - Considering categorical data
Table proportions, bar graphs, mosaic plots, and more
2.3 - Case study
Early inference ideas: testing using randomization
Google Slides & LaTeX variants available Slides for each section
Slides 2 - Summarizing data
LaTeX slides for full chapter on Github
Slides 2.1 - Examining numerical_data
Google Slides version, can export to Powerpoint
Slides 2.2 - Considering categorical data
Google Slides version, can export to Powerpoint
Slides 2.3 - Case study
Google Slides version, can export to Powerpoint
Lab - Introduction to data
Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata
Class Activity: Descriptive Measures
Collecting and exploring Instagram data
Weighted mean
Supplemental section: How and when to use weighting
Chapter 3: Probability
Probability: 3 videos Videos for some sections
3.1 - Defining probability
Core concepts, explained in detail
3.2 - Probability trees
Useful tool for conditional probability
Would you take this bet?
Thinking through probability and risk
Google Slides & LaTeX variants available Slides for each section
Slides 3 - Probability
LaTeX slides for full chapter on Github
Slides 3.1 - Defining probability
Google Slides version, can export to Powerpoint
Slides 3.2 - Conditional probability
Google Slides version, can export to Powerpoint
Slides 3.3 - Sampling from a small population
Google Slides version, can export to Powerpoint
Slides 3.4 - Random variables
Google Slides version, can export to Powerpoint
Slides 3.5 - Continuous distributions
Google Slides version, can export to Powerpoint
Lab - Probability
Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata
Chapter 4: Distributions
Distributions: 3 videos Videos for some sections
4.1 - Normal distribution
Core concepts and several examples
4.3A - Binomial distribution
Introduction to the binomial distribution
4.3B - Normal approximation to binomial
A useful technique for some binomial situations
Google Slides & LaTeX variants available Slides for each section
Slides 4 - Distributions
LaTeX slides for full chapter on Github
Slides 4.1 - Normal distributions
Google Slides version, can export to Powerpoint
Slides 4.2 - Geometric distribution
Google Slides version, can export to Powerpoint
Slides 4.3 - Binomial distribution
Google Slides version, can export to Powerpoint
Slides 4.4 - Negative binomial distribution
Google Slides version, can export to Powerpoint
Slides 4.5 - Poisson distribution
Google Slides version, can export to Powerpoint
Lab - Normal distribution
Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata
Class Activity: Sampling Distributions
As presented at Women in Stat and DS Conference
Normal distribution calculator
Online tool for normal distribution calculations
Chapter 5: Foundations for Inference
Foundations for Inference: 4 videos Videos for each section
5.1 - Variability of the sample proportion
Introduces the Central Limit Theorem
5.2 - Confidence intervals
Reporting a range, not just a point estimate
5.3 - Hypothesis testing
Introduced using numerical data (means)
Inference for other estimators
Generalizing the tools of inference
Why do we use 0.05 as a significance level?
Inquiring minds want to know -- let's explore!
Google Slides & LaTeX variants available Slides for each section
Slides 5 - Foundations for inference
LaTeX slides for full chapter on Github
Slides 5.1 - Point estimates and sampling variability
Google Slides version, can export to Powerpoint
Slides 5.2 - Confidence intervals
Google Slides version, can export to Powerpoint
Slides 5.3 - Hypothesis testing
Google Slides version, can export to Powerpoint
Lab - Intro to inference
Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata
Lab - Confidence levels
Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata
One-page inference guide
Covers one-sample and diff of means and proportions
Chapter 6: Inference for Categorical Data
Inference for categorical data: 3 videos Videos for each section
6.1 + 6.2 - Inference for proportions
Covers both 1 and 2 proportion scenarios
6.3 - Testing for goodness of fit
Chi-square test for one-way tables
6.4 - Chi-square for two-way tables
Testing for homogeneity or independence
Google Slides & LaTeX variants available Slides for each section
Slides 6 - Inference for categorical data
LaTeX slides for full chapter on Github
Slides 6.1 - Inference for a single proportion
Google Slides version, can export to Powerpoint
Slides 6.2 - Inference for a difference of two props
Google Slides version, can export to Powerpoint
Slides 6.3 - Testing goodness of fit using chi-square
Google Slides version, can export to Powerpoint
Slides 6.4 - Testing for independence in 2-way tables
Google Slides version, can export to Powerpoint
Lab - Inference for categorical data
Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata
Hypothesis testing for small sample proportions
Supplemental section: When the success-failure condition fails
Online app for Central Limit Theorem for proportions
This is a Shiny app for exploration
Chapter 7: Inference for Numerical Data
Inference for categorical data: 8 videos Videos for each section
7.1A - t-distribution
Useful new distribution for inference for means
7.1B - Inference for one mean
Covers confidence intervals and hypothesis tests
7.2 - Paired data
Special case for difference of two means
7.3 - Difference of two means
When we have two independent samples
7.4 - Power calculations
Covers the scenario of the difference of two means
7.5A - Intro to ANOVA
Key concepts and ideas
7.5B - Conditions for ANOVA
How to check if ANOVA is reasonable
7.5C - Multiple comparisons
How we determine which groups are different
Google Slides & LaTeX variants available Slides for each section
Slides 7 - Inference for numerical data
LaTeX slides for full chapter on Github
Slides 7.1 - One-sample means with the t-distribution
Google Slides version, can export to Powerpoint
Slides 7.2 - Paired data
Google Slides version, can export to Powerpoint
Slides 7.3 - Difference of two means
Google Slides version, can export to Powerpoint
Slides 7.4 - Power calculations for difference of means
Google Slides version, can export to Powerpoint
Slides 7.5 - Comparing many means with ANOVA
Google Slides version, can export to Powerpoint
Lab - Inference for numerical data
Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata
Class Activity: Correlation
Students compare and correlate movie ratings
Sample size and power (one-sample)
Supplemental section: on power in the one-sample scenario
Better understand ANOVA calculations
Supplemental section: Details behind ANOVA
Online app for Central Limit Theorem for means
This is a Shiny app for exploration
Chapter 8: Introduction to Linear Regression
Intro to linear regression: 5 videos Videos for each section
8.1 - Ideas of fitting a line
Also covers residuals and correlation
8.2 - Fitting a least squares regression line
The notion of a "best fitting" line
8.2 - Detailed Overview: Fitting a least squares regression line
Section 8.2 textbook walkthrough by author
8.3 - Types of outliers in regression
Points of high leverage and influential points
8.4 - Inference for linear regresion
Using the t-distribution for inference in regression
Google Slides & LaTeX variants available Slides for each section
Slides 8 - Linear regression
LaTeX slides for full chapter on Github
Slides 8.1 - Line fitting, residuals, and correlation
Google Slides version, can export to Powerpoint
Slides 8.2 - Fitting a line by least squares regression
Google Slides version, can export to Powerpoint
Slides 8.3 - Types of outliers in linear regression
Google Slides version, can export to Powerpoint
Slides 8.4 - Inference for linear regression
Google Slides version, can export to Powerpoint
Lab - Linear regression
Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata
Chapter 9: Multiple and Logistic Regression
Multiple & logistic regression: 4 videos Videos for some sections
9.1 - Multiple regression basics
Using many predictors in a single model
9.2 - Model selection
How to determine which variables to keep in the model
9.3 - Checking conditions using graphs
Several key graphs to assessing a multiple regression model
9.5 - Intro to logistic regression
When the outcome is binary (e.g. yes/no)
Google Slides & LaTeX variants available Slides for each section
Slides 9 - Multiple + logistic regression
LaTeX slides for full chapter on Github
Slides 9.1 - Intro to multiple regression
Google Slides version, can export to Powerpoint
Slides 9.2 - Model selection
Google Slides version, can export to Powerpoint
Slides 9.3 - Checking model conditions using graphs
Google Slides version, can export to Powerpoint
Slides 9.5 - Intro to logistic regression
Google Slides version, can export to Powerpoint
Lab - Multiple regression
Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata
More inference for linear regression
Supplemental section: Confidence and prediction intervals
Interaction terms
Supplemental section: When predictors impact outcomes in complex ways
Regression for nonlinear relationships
Supplemental section: When a straight line doesn't make sense
Online app for better understanding regression
This is a Shiny app for exploration
More Resources
Sample Student Projects