StatLens for IMSStatLens

StatLens is a free, WCAG-accessible, mobile-friendly statistics toolkit. Like Introduction to Modern Statistics (IMS) by Çetinkaya-Rundel & Hardin, it is simulation-first — it teaches inference through bootstrapping and randomization before mathematical models — and it uses OpenIntro datasets throughout. This page maps every StatLens tool to the IMS chapter it supports, so if you teach from IMS you can find the right tool for each chapter.

StatLens uses OpenIntro's datasets (CC BY-SA) and shares IMS's simulation-first approach. It is an independent community project — not affiliated with or endorsed by OpenIntro — and it is not built to follow the IMS chapter order (StatLens groups its own tools by statistical procedure). This page is a cross-reference for IMS instructors; the chapter numbers below follow IMS. StatLens is offered back to the community as free, open-source software (AGPL-3.0).

Where inference is involved, StatLens offers up to four views of the same question: Explore the data, a Bootstrap CI and a Randomization test (simulation), and a traditional Confidence Interval and Hypothesis Test. CI pages are marked with a left bar.

Exploratory data analysis

Study design · IMS Ch 2

Exploring categorical data · IMS Ch 4

Exploring numerical data · IMS Ch 5

Regression modeling

Linear regression with a single predictor · IMS Ch 7

Foundations of inference

Confidence intervals with bootstrapping · IMS Ch 12

Inference with mathematical models · IMS Ch 13

Decision errors · IMS Ch 14

Inference for categorical data

Inference for a single proportion · IMS Ch 16

Inference for comparing two proportions · IMS Ch 17

Inference for numerical data

Inference for a single mean · IMS Ch 19

Inference for comparing two independent means · IMS Ch 20

Inference for paired means · IMS Ch 21

Inference for comparing many means (ANOVA) · IMS Ch 22

Inference for regression

Inference for linear regression with a single predictor · IMS Ch 24

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