Think Stats: Probability and Statistics for Programmers Version 1. 6. 0 Think Stats Probability and Statistics for Programmers Allen B. Downey Green Tea Press Needled, Massachusetts Copyright 2011 Allen B.
Downey. 9 Washburn Eave Needled MA 02492 Permission is granted to copy, distribute, and/or modify this document under the terms of the Creative Commons Attribution-Noncommercial 3. 0 Unproved License, which is available at http://creationism's. Org/licenses/by-NC/3.
O/. A The original form of this book is L TEX source code.Compiling this code has the effect of generating a device-independent representation of a textbook, which can be inverted to other formats and printed. The L TEX source for this book is available from http://thinking's. Com. The cover for this book is based on a photo by Paul Fries (http://flick.
Com/ people/ freely/), who made it available under the Creative Commons Attribution license. The original photo is at http://flick. Com/photos/freely/11999738/. Preface Why I wrote this book Think Stats: Probability and Statistics for Programmers is a textbook for a new kind of introductory probe-stats 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 understanding.
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 (CLC) by generating samples from several distributions.When they see that the sum of values from a Parent distribution doesn't converge to normal, they remember the assumptions the CLC is based on.
Some ideas that are hard to grasp mathematically are easy to understand by simulation. For example, we approximate p-values by running 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 engage (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 particular statistics tool. Chapter O. Preface The book lends itself to a project-based approach.In my class, students work on a semester-long project that requires them to pose a statistical question, find a dataset that can address it, and apply each of the techniques they learn to their own data.
To demonstrate the kind of analysis I want students to do, the book presents a case study that runs through all of the chapters. It uses data from two sources: The National Survey of Family Growth (NSF), conducted by the U. S. Centers for Disease Control and Prevention (CDC) to gather "information on family life, marriage and divorce, pregnancy, infertility, use of contraception, and men's and women's health. (See http://CDC.
Gob/inch/NSF. HTML. ) The Behavioral Risk Factor Surveillance System (BRASS), conducted by the National Center for Chronic Disease Prevention and Health Promotion to "track health conditions and risk behaviors in the United States. " (See http://CDC. Gob/BRASS/. ) Other examples use data from the IRS, the U.
S. Census, and the Boston Marathon. How I wrote this book When people write a new textbook, they usually start by reading a stack of old textbooks. As a result, most books contain the same material in pretty much the same order.Often there are phrases, and errors, that propagate from one book to the next; Stephen Jay Gould pointed out an example in his essay, "The Case of the Creeping Fox Terriers . I did not do that.
In fact, I used almost no printed material while I was writing this book, for several reasons: My goal was to explore a new approach to this material, so I didn't want much exposure to existing approaches. Since I am making this book available under a free license, I wanted to make sure that no part of it was encumbered by copyright restrictions. Breed of dog that is about half the size of a Hermeneutic (see http://wisped. Erg/wick/Hermeneutic). IA Many readers of my books don't have access to libraries of printed material, so I tried to make references to resources that are freely available on the Internet. Proponents of old media think that the exclusive use of electronic resources is lazy and unreliable.
They might be right about the first part, but I think they are wrong bout the second, so I wanted to test my theory. The resource I used more than any other is Wisped, the bugbear of librarians everywhere. In general, the articles I read on statistical topics were very good (although I made a few small changes along the way).I include references to Wisped pages throughout the book and I encourage you to follow those links; in many cases, the Wisped page picks up where my description leaves off. The vocabulary and notation in this book are generally consistent with Wisped, unless I had a good reason to deviate.
Other resources I found useful were Wolfram Matchwood and (of course) Google. I also used two books, David Mackey's Information Theory, Inference, and Learning Algorithms, which is the book that got me hooked on Bayesian statistics, and Press et al. 's Numerical Recipes in C.But both books are available online, so I don't feel too bad. Needled MA Allen B.
Downey is a Professor of Computer Science at the Franklin W. Olin College of Engineering. Contributor List If you have a suggestion or correction, please send email to downey@allendowney. Com.
If I make a change based on your feedback, I will add you to the contributor list (unless you ask to be omitted). If you include at least part of the entente the error appears in, that makes it easy for me to search. Page and section numbers are fine, too, but not quite as easy to work with.Thanks! Lisa Downey and June Downey read an early draft and made many corrections and suggestions. Steven Ghana found several errors.
Andy Patten and Molly Affairs helped debug some of the solutions, and Molly spotted several typos. Andrew Hein found an error in my error function. Dry. Nikolas Cerebral knows how big a Hermeneutic is.
Alex Morrow clarified one of the code examples. Jonathan Street caught an error in the nick of time.
