Muutke küpsiste eelistusi

Monty the Null Hippopotamus: Exploring Statistical Foundations Through Simulation [Pehme köide]

(Virginia Tech Department of Statistics, USA)
  • Formaat: Paperback / softback, 365 pages, kõrgus x laius: 254x178 mm, 18 Tables, black and white; 82 Line drawings, black and white; 82 Illustrations, black and white
  • Ilmumisaeg: 20-May-2026
  • Kirjastus: CRC Press
  • ISBN-10: 1041256825
  • ISBN-13: 9781041256823
Teised raamatud teemal:
  • Pehme köide
  • Hind: 79,94 €
  • See raamat ei ole veel ilmunud. Raamatu kohalejõudmiseks kulub orienteeruvalt 3-4 nädalat peale raamatu väljaandmist.
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
  • Formaat: Paperback / softback, 365 pages, kõrgus x laius: 254x178 mm, 18 Tables, black and white; 82 Line drawings, black and white; 82 Illustrations, black and white
  • Ilmumisaeg: 20-May-2026
  • Kirjastus: CRC Press
  • ISBN-10: 1041256825
  • ISBN-13: 9781041256823
Teised raamatud teemal:

Monty the Null Hippopotamus: Exploring Statistical Foundations Through Simulation offers a revolutionary approach to introductory statistics designed for students who grew up in the computing age. Breaking away from the traditional formula-memorization approach that makes "Stat 101" a dreaded requirement, this 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 relying on memorizing inscrutable formulas, punching in tedious calculations into calculators, and flipping through pages of statistical 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. Every concept is illustrated with fully reproducible R code (with Python alternatives available), ensuring readers develop genuine intuition for how statistical methods actually work in practice.

Features:

• Emphasizes simulation and Monte Carlo methods over formula memorization

• Every statistical concept illustrated with fully reproducible R and Python examples

• 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 homework exercises

• Favors understanding over pedantry, with extensive web links and contextual references

The book is designed for undergraduate students comfortable with probability, calculus, and basic programming concepts. 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 narrative emphasizes active reading and coding practice rather than passive consumption, making it perfect for students who are ready to engage deeply with statistical concepts through hands-on exploration.



Emphasizes computational thinking, conceptual understanding through hands-on coding examples. Aims to make statistics accessible and engaging by leveraging modern computing power rather than relying on memorizing inscrutable formulas.

1 Toss up: coin flips. 2 Location: mean modeling. 3 A little math:
inference. 4 A little math: asymptotics. 5 Two samples. 6 Analysis of
variance. 7 Correlation & linear regression. 8 Bootstrap & permutation. 9
Non-P location. 10 Pearson ² tests. 11 Non-P scale. 12 Non-P correlation &
regression. 13 Fancy regression.
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.