Think Stats: Probability and Statistics for Programmers
By Allen B. Downey
Publisher: O'Reilly Media
Final Release Date: July 2011
Pages: 138

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
  

Table of Contents
    Chapter 1 Statistical Thinking for Programmers
        Do First Babies Arrive Late?
        A Statistical Approach
        The National Survey of Family Growth
        Tables and Records
        Significance
        Glossary
    Chapter 2 Descriptive Statistics
        Means and Averages
        Variance
        Distributions
        Representing Histograms
        Plotting Histograms
        Representing PMFs
        Plotting PMFs
        Outliers
        Other Visualizations
        Relative Risk
        Conditional Probability
        Reporting Results
        Glossary
    Chapter 3 Cumulative Distribution Functions
        The Class Size Paradox
        The Limits of PMFs
        Percentiles
        Cumulative Distribution Functions
        Representing CDFs
        Back to the Survey Data
        Conditional Distributions
        Random Numbers
        Summary Statistics Revisited
        Glossary
    Chapter 4 Continuous Distributions
        The Exponential Distribution
        The Pareto Distribution
        The Normal Distribution
        Normal Probability Plot
        The Lognormal Distribution
        Why Model?
        Generating Random Numbers
        Glossary
    Chapter 5 Probability
        Rules of Probability
        Monty Hall
        Poincaré
        Another Rule of Probability
        Binomial Distribution
        Streaks and Hot Spots
        Bayes’s Theorem
        Glossary
    Chapter 6 Operations on Distributions
        Skewness
        Random Variables
        PDFs
        Convolution
        Why Normal?
        Central Limit Theorem
        The Distribution Framework
        Glossary
    Chapter 7 Hypothesis Testing
        Testing a Difference in Means
        Choosing a Threshold
        Defining the Effect
        Interpreting the Result
        Cross-Validation
        Reporting Bayesian Probabilities
        Chi-Square Test
        Efficient Resampling
        Power
        Glossary
    Chapter 8 Estimation
        The Estimation Game
        Guess the Variance
        Understanding Errors
        Exponential Distributions
        Confidence Intervals
        Bayesian Estimation
        Implementing Bayesian Estimation
        Censored Data
        The Locomotive Problem
        Glossary
    Chapter 9 Correlation
        Standard Scores
        Covariance
        Correlation
        Making Scatterplots in Pyplot
        Spearman’s Rank Correlation
        Least Squares Fit
        Goodness of Fit
        Correlation and Causation
        Glossary

    Colophon