Think Stats: Exploratory Data AnalysisIf you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts. New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries.

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Contents
Section 21  
Section 22  
Section 23  
Section 24  
Section 25  
Section 26  
Section 27  
Section 28  
Section 9  
Section 10  
Section 11  
Section 12  
Section 13  
Section 14  
Section 15  
Section 16  
Section 17  
Section 18  
Section 19  
Section 20  
Section 29  
Section 30  
Section 31  
Section 32  
Section 33  
Section 34  
Section 35  
Section 36  
Section 37  
Section 38  
Section 39  
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Common terms and phrases
agepreg analytic distribution autocorrelation babies birth weight BRFSS Central Limit Theorem chapter ChiSquared Test Classical Hypothesis Cohort Effects confidence interval Correlation Test DataFrame dataset Difference in Means Distribution Framework estimated parameters Estimation Game example Exercises Expected Remaining Lifetime explanatory variables exponential distribution Figure Glossary groups hazard function HazardFunction Here’s the code Hist histogram hypothesis testing Implementation integer Least Squares Fit Linear Model linear regression logistic regression lognormal Distribution median method mother’s age Moving Averages normal distribution normal probability plot NSFG Variables null hypothesis NumPy ofthe pandas Pareto Distribution PDFs Pearson’s correlation percentile rank population predictions pregnancy length provides pvalue Python Random Numbers residuals respondents RMSE sampling distribution sampling error Scatter Plots sequence serial correlation shows the result simulated skewness slope Spearman’s Rank Correlation standard deviation standard error statistically significant StatsModels Survey of Family Survival Curves survival function SurvivalFunction test statistic test_stat thinkstats2 variance Weighted Resampling