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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apparent effect average babies Bayes’s theorem Bayesian estimation Bayesian Probabilities birth weights born during Week BRFSS CCDF Central Limit Theorem chance chapter chi-square 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 interarrival likelihood linear live births maps from values measure median Monty Monty Hall problem normal distribution normal probability plot NSFG null hypothesis number of values outliers p-value Pareto distribution percentile rank population posterior pregnancy lengths prior prob problem pyplot Python random numbers random sample random variable relationship result runners sample mean scatterplot scores sequence simulation skewness standard deviation statistically significant summary statistic takes variance Weibull distribution Wikipedia Write a function