Introduction to
Modern Statistics
Bringing a fresh approach to intro statistics, IMS introduces multi-dimensional thinking early on and uses both simulation techniques and traditional methods
Introduction to Modern Statistics is a re-imagining of a previous title, Introduction to Statistics with Randomization and Simulation. The new book puts a heavy emphasis on exploratory data analysis (specifically exploring multivariate relationships using visualization, summarization, and descriptive models) and provides a thorough discussion of simulation-based inference using randomization and bootstrapping, followed by a presentation of the related Central Limit Theorem based approaches. The second edition of IMS has updated datasets, additional exercises, a new application for chapter 3, and updated text and code to reflect changes in best practices. Other highlights include:
Web native book. The online book is available in HTML, which offers easy navigation and searchability in the browser. The book is built with the bookdown package and the source code to reproduce the book can be found on GitHub. Along with the bookdown site, this book is also available as a PDF and in paperback. Read the book online here.
Tutorials. While the main text of the book is agnostic to statistical software and computing language, each part features 4-8 interactive R tutorials (for a total of 32 tutorials) that walk you through the implementation of the part content in R with the tidyverse for data wrangling and visualisation and the tidyverse-friendly infer package for inference. The self-paced and interactive R tutorials were developed using the learnr R package, and only an internet browser is needed to complete them. Browse the tutorials here.
Labs. Each part also features 1-2 R based labs. The labs consist of data analysis case studies and they also make heavy use of the tidyverse and infer packages. View the labs here.
Datasets. Datasets used in the book are marked with a link to where you can find the raw data. The majority of these point to the openintro package. You can install the openintro package from CRAN or get the development version on GitHub. Find out more about the package here.
Getting Started
We are in the process of updating to the 2nd Edition, with the main PDF on Leanpub now reflecting the new edition as well as the HTML and paperback are now available in the 2nd Edition. The 1st Edition PDF may be found with the free "Extras" when purchasing the PDF through the link below on Leanpub (including "purchases" for $0), and other 1st Edition links are also available below.
Amazon KDP raised book print prices by ≈40% in 2023. These changes will net Amazon over $50,000 per year from sales on OpenIntro books, and we expect that OpenIntro will lose money as a result of reduced sales. We are continuing to explore alternative printers to Amazon that provide better quality books as well as selling more books outside of Amazon.
All of our website / resource links to Amazon are affiliate links. When you shop on Amazon using these links, we receive a small commission at no extra charge to you.
FREE -- Textbook PDF
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FREE -- 2nd Edition on Web
Freely available online
$25 -- B&W paperback (2nd Edition)
Available on Amazon and in select bookstores
FREE -- 1st Edition on Web
Freely available online
$25 -- B&W paperback (1st Edition)
Available on Amazon and in select bookstores
Teachers page with additional resources
Some public resources, others restricted to Verified Teachers
Send feedback or report a typo
We appreciate feedback, both positive and negative
List of known textbook typos
Review textbook typos and clarifications
Data sets
List of data sets and the option to download files
Teachers
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
IMS2 exercise solutions online
Available to Verified Teachers, click here to apply for access
IMS1 exercise solutions online
Available to Verified Teachers, click here to apply for access
IMS, Sample Course Plan
Jo Hardin, Pomona College
Teachers page with additional resources
Some public resources, others restricted to Verified Teachers
What changed in the 2nd edition
Overview of the updates made
Teachers: Sample Exams
Restricted to Verified Teachers only. The sample exams currently available are from our other textbooks. We plan to add sample exams for IMS soon.
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
OpenIntro Statistics, Sample Exams (Adam Gilbert)
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 videos 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
Part 1: Introduction to data
Tutorial: Introduction to data
Online interactive R tutorial
Lab - Intro to Statistical Software
Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, SAS, Stata
Part 2: Exploratory data analysis
Tutorial: Exploratory data analysis
Online interactive R tutorial
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
Part 3: Regression modeling
Tutorial: Regression modeling
Online interactive R tutorial
Lab - Linear regression
Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata
Part 4: Foundations of inference
Tutorial: Foundations of inference
Online interactive R tutorial
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
Part 5: Statistical inference
Tutorial: Statistical inference
Online interactive R tutorial
Lab - Inference for categorical data
Software: R (Base), R (Tidyverse), Rguroo, Jamovi, JASP, Python, SAS, Stata
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
Part 6: Inferential modeling
Tutorial: Inferential modeling
Online interactive R tutorial
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
More Content
This section is for content not covered in IMS that might be of interest to a subset of instructors. For example, some courses require Probability as a topic covered, and so we are providing an online-only supplement to support such a scenario.
Probability supplemental chapter
Source: OpenIntro Statistics, 4th Edition
Probability supplement, odd-exercise solutions
Source: OpenIntro Statistics, 4th Edition