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 than 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.
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.
- Video "vignettes" are provided as a quick introduction to chapter material. They are no substitue for reading/thinking critically, and are deliberately incomplete.
- 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
Like Dave's Hipp0 or want to support the book but don't need a paper copy?
Chapters
-
Chapter 1: Toss up: coin flips (summary)
Python: chap1.py
Vignette: coin flips -
Chapter 2: Location: mean modeling (summary)
Supplementary R: pval_discrete.R
Python: chap2.py
Data: roi.RData or roi.pkl
Vignette: mean modeling -
Chapter 3: A little math: inference (summary)
Python: chap3.py
Vignettes: (a) likelihood, (b) inference and (c) confidence intervals -
Chapter 4: A little math: asymptotics (summary)
Python: chap4.py
Vignettes: (a) central limit theorem and (b) asymptotic distribution of the MLE -
Chapter 5: Two samples (summary)
Python: chap5.py
Data: roi.RData or roi.pkl, roip.csv
Vignettes: (a) Bernoulli and (b) Gaussian (Welch & paired-t) -
Chapter 6: Analysis of variance (summary)
Python: chap6.py
Data: roi.RData or roi.pkl
Vignettes: (coming soon) -
Chapter 7: Correlation and linear regression (summary)
Python: chap7.py
Data: bloodpres.csv, wages.csv, rent.csv
Vignettes: (coming soon) -
Chapter 8: Bootstrap and permutation (summary)
Python: chap8.py
Data: roi.RData or roi.pkl, rent.csv, memory.RData
Vignettes: (coming soon) -
Chapter 9: Non-P location (summary)
Supplementary R: ranks.R, rank_ties.R
Python: chap9.py
Data: roi.RData, roip.csv, classmode.RData
Vignettes: (coming soon) -
Chapter 10: Pearson chi-squared tests (summary)
Python: chap10.py
Data: roi.RData, nels_math.csv, assault.csv
Vignettes: (coming soon) -
Chapter 11: Non-P scale (summary)
Supplementary R: ranks.R
Python: chap11.py
Data: roi.RData, tv.csv, bacteria.csv
Vignettes: (coming soon) -
Chapter 12: Non-P correlation and regression (summary)
Supplementary R: rank_ties.R
Python: chap12.py
Data: nels_math.csv, usprecip.csv, voting.csv, VAjantemp.csv, rent.csv, mileage.csv, imports.csv
Vignettes: (coming soon) -
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
Vignettes: (coming soon) -
Appendices: (summary)
A: Loaded terms
B: Coded subroutines