Download Introduction to Modern Statistics (2^nd Ed.) — Free PDF + Companion Resources

Looking for a beginner-friendly yet modern statistics textbook? The OpenIntro team’s Introduction to Modern Statistics (IMS) is 100 % free under a Creative Commons BY-SA 3.0 license. In over 510 pages, it teaches all the statistical fundamentals you’ll need for data-science work: exploratory analysis, simulation-based inference, regression modelling, and more

This permanent page hosts the latest PDF, solution manual, lecture slides, and clean datasets so beginners and STEM students can dive straight into practice—no scavenger hunt required.

Why Every Data-Science Learner Needs a Solid Statistics Core

Statistical thinking underpins virtually every data-science workflow, from A/B testing to machine-learning model validation. Concepts like confidence intervals, hypothesis testing, and regression diagnostics help you decide whether a pattern in your data is real or just noise

What You’ll Find Inside Introduction to Modern Statistics

• 510 pages, twelve chapters. Learn data wrangling, visual EDA, probability, inference, ANOVA, multiple regression, and more
• Simulation-first approach. Complex ideas (e.g. p-values) are introduced via bootstrapping and randomisation before formal maths— ideal for visual learners
• Hundreds of worked examples + an appendix with full exercise solutions for self-study.
• Open datasets. All chapters reference real-world data you can download instantly from the openintro R package or CSV links.

Bonus Companion Files (All Free)

1. Solution Manual (PDF). Step-by-step answers to every odd-numbered exercise
2. Lecture Slide Decks (PDF & Keynote). 200+ instructor slides covering each chapter’s learning objectives and examples
3. Datasets Folder (CSV + RDS). Clean versions of all IAM-tagged tables—ready for R, Python, or Excel.

Suggested Study Workflow

1. Read → Skim. Outline each chapter, note the example code you want to replicate.
2. Practice. Load the matching dataset, run the example analysis, then tweak variables to cement understanding.
3. Apply. Pick an open dataset from Kaggle or data.gov and replicate an analysis workflow end-to-end.
4. Document. Publish a short write-up on your GitHub or personal blog—employing best-practice statistical language.

📥 Direct Download Links

All files are hosted on our public Storage archive for one-click access Join @Coursejoint on Telegram to stay up to date and save newer resources once available.

• IMS (2^e) PDF  ↗
• Solution Manual ↗
• Lecture Slides ↗
• Datasets ↗

License & Attribution

The book and companion files are released by the OpenIntro Foundation under the Creative Commons CC BY-SA 3.0 license. You are free to share and adapt the material as long as you provide proper attribution and release any derivative work under the same license.

More Free Statistics Textbooks You Might Like

• OpenIntro Statistics — a classic intro text with extra chapters on ANOVA and multiple regression.
• OpenIntro Statistics (4e) on Open Textbook Library — easily remixed into Canvas or Moodle courses
• IMS GitHub Repo — pull LaTeX source, build slides, or file errata.

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