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


Teachers


Teachers: Sample Exams


What is Statistics?


Part 1: Introduction to data


Part 2: Exploratory data analysis


Part 3: Regression modeling


Part 4: Foundations of inference


Part 5: Statistical inference


Part 6: Inferential modeling


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