Monty the Null Hippopotamus : Exploring Statistical Foundations Through Simulation

Monty the Null Hippopotamus: Exploring Statistical Foundations Through Simulation offers 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, the book 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.

The book covers foundational statistical concepts through advanced topics including nonparametric methods and regression analysis, all presented through a computational lens that prioritizes process over precision. Everything is illustrated with coded examples, ensuring readers develop genuine intuition for how statistical methods actually work in practice.

Features

• Emphasizes simulation and Monte Carlo methods over formula memorization

• Illustrated with fully reproducible R and supplementary Python

• Comprehensive coverage, from basic inference to nonparametrics and regression

• 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

This book is designed for undergraduate students comfortable with probability, calculus, and basic programming. It serves as an ideal text for introductory statistics courses, and is equally valuable for graduate students from non-statistical backgrounds seeking a modern foundation in statistical thinking. The text is perfect for students ready to engage deeply with statistical concepts through hands-on exploration.

Robert B. Gramacy is a professor of statistics at Virginia Tech (VT), and a fellow of the American Statistical Association. At VT, he also serves as an affiliate faculty member in the Computational Modeling and Data Analytics program, and as a core faculty member in the Center for Ecosystem Forecasting. His research interests include: Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, surrogate modeling, uncertainty quantification, sequential design, and optimization under uncertainty.