Think StatsIf 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.

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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 
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Common terms and phrases
apparent effect average babies Bayes’s theorem Bayesian estimation Bayesian Probabilities birth weights born during Week BRFSS CCDF Central Limit Theorem chance chapter chisquare choose coefficient compute the distribution compute the mean confidence interval continuous distribution dataset difference in mean download a solution Erlang distribution evidence example Exercise exponential distribution Figure frequency function called function named Glossary groups guess Here’s Hist objects Histograms hypothesis testing likelihood linear live births lognormal maps from values measure median Monty Monty Hall problem normal distribution NSFG null hypothesis number of values outliers pvalue Pareto distribution Pearson’s correlation percentile rank population posterior pregnancy length prior prob problem pyplot Python random numbers random variable relationship result runners sample mean scatterplot scores sequence simulation skewness standard deviation statistically significant summary statistic variance Weibull distribution Wikipedia Write a function