Introductory Statistics with Randomization and Simulation
Bringing a fresh approach to intro statistics, ISRS introduces inference faster using randomization and simulation techniques
A new book, Introduction to Modern Statistics (IMS is available on the web, as a PDF, and in paperback), represents the evolution of Introductory Statistics with Randomization and Simulation (ISRS). For those who are considering adopting Introductory Statistics with Randomization and Simulation, we recommend Introduction to Modern Statistics instead.
This book is now de-emphasized in our catalog, e.g. moved to last in our list of books available and we no longer offer wholesale discounts on this title to bookstores.
Getting Started
Videos and some slides listed make use of materials from OpenIntro Statistics. Don't fret if you see a different section number than the one you expect: check the title and you'll see it is covering the topic of interest.
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$8.50 -- B&W paperback
Available on Amazon and in select bookstores
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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
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
Intro Stat w/Rand & Sim exercise solutions
Available to Verified Teachers, click here to apply for access
Bookstore Ordering (bulk)
Wholesale purchase options
Teachers page with additional resources
Some public resources, others restricted to Verified Teachers
Teachers: Sample Exams
Restricted to Verified Teachers only.
ISRS, Sample Midterms and Final Exam (Albert Kim)
Available to Verified Teachers, click here to apply for access
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
ISLBS, Sample Midterm and Final Exams (Julie Vu)
Available to Verified Teachers, click here to apply for access
What is Statistics?
These resources give a taste of what statisticians, also known as data scientists, do in the real world.
200 years of history through health & wealth
A tour with Hans Rosling
Bringing life to global health statistics
A longer tour led by Hans Rosling
Building a better NBA team through analytics
Ivana Seric, college basketball player becomes a data scientist
Steven Levitt explores the value of car seats
Disclaimers at the end
Using statistics to treat chronic illnesses
MacArthur Fellow Susan Murphy
Using data to better understand agriculture
MacArthur Fellow David Lobell
Why the term "Data Science" is so confusing
The two main types of data scientists: Analysis and Building
Chapter 1: Intro to Data
1.1 - Using stents to prevent strokes
Real case study with a surprising finding
1.2 - Data basics
Typical data structures and properties
1.3 - Data collection principles
Thoughtful data collection is critical to learning from data
1.4 - Sampling principles and strategies
Different ways to sample from a population
1.5 - Experiments
Basic principles of experimental design
1.6 - Examining numerical data
Mean, standard deviation, histograms, box plots, and more
1.7 - Considering categorical data
Table proportions, bar graphs, mosaic plots, and more
Slides 1A - Intro to data
LaTeX slides for full chapter on Github
Slides 1B - Summarizing 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 + 1.4 - Sampling principles and strategies
Google Slides version, can export to Powerpoint
Slides 1.5 - Experiments
Google Slides version, can export to Powerpoint
Slides 1.6 - Examining numerical_data
Google Slides version, can export to Powerpoint
Slides 1.7 - Considering categorical data
Google Slides version, can export to Powerpoint
Lab - Intro to Statistical Software
Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, SAS, Stata
Lab - Introduction to data
Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata
Weighted mean
Supplemental section: How and when to use weighting
Chapter 2: Foundations for Inference
2.1 - Case study
Early inference ideas: testing using randomization
2.6 - Normal distribution
Core concepts and several examples
2.8 - Confidence intervals
Reporting a range, not just a point estimate
Slides 2.1 - Case study
Google Slides version, can export to Powerpoint
Slides 2.6 - Normal distributions
Google Slides version, can export to Powerpoint
Slides 2.8 - Confidence intervals
Google Slides version, can export to Powerpoint
Lab - Normal distribution
Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata
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
Normal distribution calculator
Online tool for normal distribution calculations
Chapter 3: Inference for Categorical Data
3.1 + 3.2 - Inference for proportions
Covers both 1 and 2 proportion scenarios
3.3 - Testing for goodness of fit
Chi-square test for one-way tables
3.4 - Chi-square for two-way tables
Testing for homogeneity or independence
Slides 3 - Inference for categorical data
LaTeX slides for full chapter on Github
Slides 3.1 - Inference for a single proportion
Google Slides version, can export to Powerpoint
Slides 3.2 - Inference for a difference of two props
Google Slides version, can export to Powerpoint
Slides 3.3 - Testing goodness of fit using chi-square
Google Slides version, can export to Powerpoint
Slides 3.4 - 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
One-page inference guide
Covers one-sample and diff of means and proportions
Chapter 4: Inference for Numerical Data
4.1A - t-distribution
Useful new distribution for inference for means
4.1B - Inference for one mean
Covers confidence intervals and hypothesis tests
4.2 - Paired data
Special case for difference of two means
4.3 - Difference of two means
When we have two independent samples
4.4A - Intro to ANOVA
Key concepts and ideas
4.4B - Conditions for ANOVA
How to check if ANOVA is reasonable
4.4C - Multiple comparisons
How we determine which groups are different
4.X - Power calculations
Covers the scenario of the difference of two means
Slides 4 - Inference for numerical data
LaTeX slides for full chapter on Github
Slides 4.1 - One-sample means with the t-distribution
Google Slides version, can export to Powerpoint
Slides 4.2 - Paired data
Google Slides version, can export to Powerpoint
Slides 4.3 - Difference of two means
Google Slides version, can export to Powerpoint
Slides 4.4 - Comparing many means with ANOVA
Google Slides version, can export to Powerpoint
Slides 4.X - Power calculations for difference of means
Google Slides version, can export to Powerpoint
Lab - Inference for numerical data
Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata
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 5: Introduction to Linear Regression
5.1 - Ideas of fitting a line
Also covers residuals and correlation
5.2 - Fitting a least squares regression line
The notion of a "best fitting" line
5.3 - Types of outliers in regression
Points of high leverage and influential points
5.4 - Inference for linear regresion
Using the t-distribution for inference in regression
Slides 5 - Linear regression
LaTeX slides for full chapter on Github
Slides 5.1 - Line fitting, residuals, and correlation
Google Slides version, can export to Powerpoint
Slides 5.2 - Fitting a line by least squares regression
Google Slides version, can export to Powerpoint
Slides 5.3 - Types of outliers in linear regression
Google Slides version, can export to Powerpoint
Slides 5.4 - 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 6: Multiple and Logistic Regression
6.1 - Multiple regression basics
Using many predictors in a single model
6.2 - Model selection
How to determine which variables to keep in the model
6.3 - Checking conditions using graphs
Several key graphs to assessing a multiple regression model
6.4 - Intro to logistic regression
When the outcome is binary (e.g. yes/no)
Slides 6 - Multiple + logistic regression
LaTeX slides for full chapter on Github
Slides 6.1 - Intro to multiple regression
Google Slides version, can export to Powerpoint
Slides 6.2 - Model selection
Google Slides version, can export to Powerpoint
Slides 6.3 - Checking model conditions using graphs
Google Slides version, can export to Powerpoint
Slides 6.4 - 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 Student Resources
Video guide: Casio fx-9750GII
Playlist covering all intro statistics topics for this calculator
Video guide: TI-83 and TI-84
Playlist for all intro statistics topics for both calculators
Casio fx-9750GII graphing calculator guide
Written guide covering intro statistics functionality
TI-83 / TI-84 graphing calculator guide
Written guide covering intro statistics functionality
Distribution calculators
Covers the normal, t, and chi-square distributions
Probability tables
Covers the normal, t, and chi-square distributions
Course Resources
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
ISRS LaTeX source files
Leads to the Github repository
Intro Stat w/Rand & Sim exercise solutions
Available to Verified Teachers, click here to apply for access
Why the term "Data Science" is so confusing
The two main types of data scientists: Analysis and Building
More Free Books
Beyond the set of textbooks we offer on openintro.org, many other authors have made their books publicly available. The links below go to the places where these other authors posted their books for free for anyone.
Survival Analysis in R
Workshop materials and a guide for survival analysis in R
Practical Regression and ANOVA using R
Related publisher title: Linear Models with R
A First Course in Design and Analysis of Experiments
Includes supplemental resources
An Introduction to Statistical Learning
Physical copies are about $80
Elements of Statistical Learning
Physical copies are about $90
Introduction to Probability
Physical copies are about $60
Think Stats
Physical copies are about $30
Collaborative Statistics on CNX.org
Physical copies are about $30 on Lulu
CK-12 Probability and Statistics
CK-12 offers many open-source textbooks
Introduction to Statistical Thought
Upper division or intro grad level
Introduction to Probability and Statistics Using R
Available on R-Forge
Online Statistics Education
An Interactive Multimedia Course of Study
Introduction to Statistical Thinking
With R, Without Calculus