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Think Stats: Probability and
Statistics for Programmers
Version 1.6.0
Think Stats
Probability and Statistics for Programmers
Version 1.6.0
Allen B. Downey
Green Tea Press
Needham, Massachusetts
Copyright © 2011 Allen B. Downey.
Green Tea Press
9 Washburn Ave
Needham MA 02492
Permission is granted to copy, distribute, and/or modify this document under
the terms of the Creative Commons Attribution-NonCommercial 3.0 Unported Li-
cense, which is available at
http://creativecommons.org/licenses/by-nc/3.0/
.
The original form of this book is L
A
T
E
X source code. Compiling this code has the
effect of generating a device-independent representation of a textbook, which can
be converted to other formats and printed.
The L
A
T
E
X source for this book is available from
http://thinkstats.com
.
The cover for this book is based on a photo by Paul Friel (
http://flickr.com/
people/frielp/
), who made it available under the Creative Commons Attribution
license. The original photo is at
http://flickr.com/photos/frielp/11999738/
.
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 a computational approach, which has several
advantages:
• Students write programs as a way of developing and testing their un-
derstanding. For example, they write functions to compute 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 and computation 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 easy computationally.
• Because students work in a general-purpose programming language
(Python), they are able to import data from almost any source. They
are not limited to data that has been cleaned and formatted for a par-
ticular statistics tool.
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