# Think Stats

"O'Reilly Media, Inc.", Jul 1, 2011 - Computers - 138 pages

If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.

• Develop your understanding of probability and statistics by writing and testing code
• Run experiments to test statistical behavior, such as generating samples from several distributions
• Use simulations to understand concepts that are hard to grasp mathematically
• Learn topics not usually covered in an introductory course, such as Bayesian estimation
• Import data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics tools
• Use statistical inference to answer questions about real-world data

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#### Review: Think Stats

User Review  - Nancy Wu - Goodreads

While I'm only halfway through this book, it teaches neither statistics nor tips/tricks with Python libraries. The github source code that accompanies the book is probably more useful as a reference ... Read full review

#### Review: Think Stats

User Review  - Goodreads

While I'm only halfway through this book, it teaches neither statistics nor tips/tricks with Python libraries. The github source code that accompanies the book is probably more useful as a reference ... Read full review

### Contents

 Chapter 1 Statistical Thinking for Programmers 1 Chapter 2 Descriptive Statistics 11 Chapter 3 Cumulative Distribution Functions 23 Chapter 4 Continuous Distributions 33 Chapter 5 Probability 47 Chapter 6 Operations on Distributions 61
 Chapter 7 Hypothesis Testing 73 Chapter 8 Estimation 85 Chapter 9 Correlation 97 Index 113 Copyright

### About the author (2011)

Allen Downey is an Associate Professor of Computer Science at the Olin College of Engineering. He has taught computer science at Wellesley College, Colby College and U.C. Berkeley. He has a Ph.D. in Computer Science from U.C. Berkeley and Master's and Bachelor's degrees from MIT.