Robert B. Gramacy Professor of Statistics
Monty the Null Hippopotamus
Exploring Statistical Foundations Through Simulation
Exploring Statistical Foundations Through Simulation
A revolutionary approach to introductory statistics for students who grew up in the computing age. Breaking away from the traditional formula-memorization approach that makes "Stat 101" a dreaded requirement, Monty emphasizes computational thinking and conceptual understanding through hands-on coding examples. The aim is to make statistics accessible and engaging by leveraging modern computing power rather the tedium of punching numbers into calculators or flipping through pages of tables.
Topics/features include:
- Foundational statistical concepts: binomial, location/Gaussain models, ANOVA, correlation
- Advanced topics: nonparametric methods and regression
- Emphasizes simulation and Monte Carlo methods over formula memorization.
- Everything illustrated with fully reproducible code.
- Graduated homework exercises combining theory with real-world data analysis.
- More than thirty data sets supporting examples and exercises.
- Favors understanding over pedantry, with extensive web links and contextual references.
Designed for undergraduates comfortable with probability, calculus, and basic programming. Serves as an ideal text for introductory statistics courses. Equally valuable for graduate students from non-statistical backgrounds seeking a modern foundation in statistical thinking. Perfect for students who are ready to engage deeply with statistical concepts through hands-on exploration.
Soft open
- This is a soft open of virtual content.
- The book will be available for purchase in April 2026.
- Some of the links across/below are placeholders.
Access and content
- Download an electronic "print version".
- Links across/below point directly to HTML renderings of the chapters. Or start from the title page.
- Please consider buying a physical copy from CRC, Amazon, Barnes & Noble, or anywhere fine books are sold.
- Python versions of R code maybe found linked with each chapter across/below. Thanks to Anya Raval.
- Please use this BibTeX entry for citation.
Errata
Comments/corrections by email are much appreciated.
- The HTML is updated in near real-time;
- grammar/cosmetic fixes addressed without fuss;
- so errata/corrections apply to major changes in print/PDF version only.
Errata text file linked here.
Merch
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Chapters
-
Chapter 1: Toss up: coin flips (summary)
Python: chap1.py -
Chapter 2: Location: mean modeling (summary)
Supplementary R: pval_discrete.R
Python: chap2.py
Data: roi.RData or roi.pkl -
Chapter 3: A little math: inference (summary)
Python: chap3.py -
Chapter 4: A little math: asymptotics (summary)
Python: chap4.py -
Chapter 5: Two samples (summary)
Python: chap5.py (coming soon)
Data: roi.RData, roip.csv -
Chapter 6: Analysis of variance (summary)
Python: chap6.py (coming soon)
Data: roi.RData -
Chapter 7: Correlation and linear regression (summary)
Python: chap7.py (coming soon)
Data: bloodpres.csv, wages.csv, rent.csv -
Chapter 8: Bootstrap and permutation (summary)
Python: chap8.py (coming soon)
Data: roi.RData, rent.csv, memory.RData -
Chapter 9: Non-P location (summary)
Supplementary R: ranks.R, rank_ties.R
Python: chap9.py (coming soon)
Data: roi.RData, roip.csv, classmode.RData -
Chapter 10: Pearson chi-squared tests (summary)
Python: chap10.py (coming soon)
Data: roi.RData, nels_math.csv, assault.csv -
Chapter 11: Non-P scale (summary)
Supplementary R: ranks.R
Python: chap11.py (coming soon)
Data: roi.RData, tv.csv, bacteria.csv -
Chapter 12: Non-P correlation and regression (summary)
Supplementary R: rank_ties.R
Python: chap12.py (coming soon)
Data: usprecip.csv, voting.csv, VAjantemp.csv, rent.csv, mileage.csv, imports.csv -
Chapter 13: Fancy regression (summary)
Supplementary R: rank_ties.R
Python: chap13.py (coming soon)
Data: pickups.csv, mammals.csv, confood.csv, grades.csv, boss.csv, telemkt.csv, elec.csv, mileage.csv, census2000.csv -
Appendices: (summary)
A: Loaded terms
B: Coded subroutines