Think Stats

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Overview

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,...

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Think Stats

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Overview

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

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.

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Product Details

  • ISBN-13: 9781449307110
  • Publisher: O'Reilly Media, Incorporated
  • Publication date: 7/22/2011
  • Edition number: 1
  • Pages: 119
  • Sales rank: 816,874
  • Product dimensions: 6.80 (w) x 9.10 (h) x 0.40 (d)

Meet the Author

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.

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Table of Contents

Preface;
Why I Wrote This Book;
How I Wrote This Book;
Contributor List;
Conventions Used in This Book;
Using Code Examples;
Safari® Books Online;
How to Contact Us;
Chapter 1: Statistical Thinking for Programmers;
1.1 Do First Babies Arrive Late?;
1.2 A Statistical Approach;
1.3 The National Survey of Family Growth;
1.4 Tables and Records;
1.5 Significance;
1.6 Glossary;
Chapter 2: Descriptive Statistics;
2.1 Means and Averages;
2.2 Variance;
2.3 Distributions;
2.4 Representing Histograms;
2.5 Plotting Histograms;
2.6 Representing PMFs;
2.7 Plotting PMFs;
2.8 Outliers;
2.9 Other Visualizations;
2.10 Relative Risk;
2.11 Conditional Probability;
2.12 Reporting Results;
2.13 Glossary;
Chapter 3: Cumulative Distribution Functions;
3.1 The Class Size Paradox;
3.2 The Limits of PMFs;
3.3 Percentiles;
3.4 Cumulative Distribution Functions;
3.5 Representing CDFs;
3.6 Back to the Survey Data;
3.7 Conditional Distributions;
3.8 Random Numbers;
3.9 Summary Statistics Revisited;
3.10 Glossary;
Chapter 4: Continuous Distributions;
4.1 The Exponential Distribution;
4.2 The Pareto Distribution;
4.3 The Normal Distribution;
4.4 Normal Probability Plot;
4.5 The Lognormal Distribution;
4.6 Why Model?;
4.7 Generating Random Numbers;
4.8 Glossary;
Chapter 5: Probability;
5.1 Rules of Probability;
5.2 Monty Hall;
5.3 Poincaré;
5.4 Another Rule of Probability;
5.5 Binomial Distribution;
5.6 Streaks and Hot Spots;
5.7 Bayes’s Theorem;
5.8 Glossary;
Chapter 6: Operations on Distributions;
6.1 Skewness;
6.2 Random Variables;
6.3 PDFs;
6.4 Convolution;
6.5 Why Normal?;
6.6 Central Limit Theorem;
6.7 The Distribution Framework;
6.8 Glossary;
Chapter 7: Hypothesis Testing;
7.1 Testing a Difference in Means;
7.2 Choosing a Threshold;
7.3 Defining the Effect;
7.4 Interpreting the Result;
7.5 Cross-Validation;
7.6 Reporting Bayesian Probabilities;
7.7 Chi-Square Test;
7.8 Efficient Resampling;
7.9 Power;
7.10 Glossary;
Chapter 8: Estimation;
8.1 The Estimation Game;
8.2 Guess the Variance;
8.3 Understanding Errors;
8.4 Exponential Distributions;
8.5 Confidence Intervals;
8.6 Bayesian Estimation;
8.7 Implementing Bayesian Estimation;
8.8 Censored Data;
8.9 The Locomotive Problem;
8.10 Glossary;
Chapter 9: Correlation;
9.1 Standard Scores;
9.2 Covariance;
9.3 Correlation;
9.4 Making Scatterplots in Pyplot;
9.5 Spearman’s Rank Correlation;
9.6 Least Squares Fit;
9.7 Goodness of Fit;
9.8 Correlation and Causation;
9.9 Glossary;
Colophon;

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Customer Reviews

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Sort by: Showing all of 4 Customer Reviews
  • Anonymous

    Posted March 30, 2014

    Very nice

    Full of good things

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  • Anonymous

    Posted July 12, 2012

    Bluemist

    "Yes. Icepool and Tigerheart are my sister's kits."

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  • Posted August 11, 2011

    Learning Statistics using Programming

    If your grasp of Programming exceeds your understanding of Basic Statistics, this book IS for you. As a University Statistics professor, I am constantly looking for reading materials that I can use to integrate Practical Statistics with programming. I am generally faced with the problem of having to mine Programming texts for Stats lessons, all too often I am faced with books that attempt to teach a programming language with examples from Freshman Statistics as an afterthought. (Too much of one, not enough of the other) This book comes at the problem from the other side. Given that you already have a healthy grasp on programming and are trying to learn Statistics, each topic is presented with helpful, real-world data examples, and a step-by-step explanation of how to code the solutions. That makes this book excellent supplementary material for a Statistics class, or at the very least, a wonderful refresher for those returning to Statistics, with programming in mind. Caution: This book is NOT for you if you do NOT have a basic understanding of Programming. This book will NOT teach you to program using statistics. It is meant to teach you statistics using programming.

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  • Anonymous

    Posted September 10, 2011

    No text was provided for this review.

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