Think Stats Probability and Statistics for Programmers - Downey.pdf

Probability and Statistics for Programmers
Think Stats
Probability and Statistics for Programmers
Version
Allen B. Downey
Green Tea Press
Needham, Massachusetts
Green Tea Press
9 Washburn Ave
Needham MA 02492
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Preface
Why I wrote this book
Think Stats: Probability and Statistics for Programmers is a textbook for a new
kind of introductory prob-stat class. It emphasizes the use of statistics to
explore large datasets. It takes putational approach, which has several
advantages:
• Students write programs as a way of developing and testing their un-
derstanding. For example, they write functions pute a least
squares fit, residuals, and the coefficient of determination. Writing
and testing this code requires them to understand the concepts and
implicitly corrects misunderstandings.
• Students run experiments to test statistical behavior. For example,
they explore the Central Limit Theorem (CLT) by generating samples
from several distributions. When they see that the sum of values from
a Pareto distribution doesn’t converge to normal, they remember the
assumptions the CLT is based on.
• Some ideas that are hard to grasp mathematically are easy to under-
stand by simulation. For example, we approximate p-values by run-
ning Monte Carlo simulations, which reinforces the meaning of the
p-value.
• Using discrete distributions putation makes it possible to
present topics like Bayesian estimation that are not usually covered
in an introductory class. For example, one exercise asks students to
compute the posterior distribution for the “German tank problem,”
which is difficult analytically but surprisingly putationally.
• Because students work in a general-purpose programming language
(Python), they are able to import data from