Code for Estimation of Population Parameters

Python notebooks for all the computational examples about Estimation of Population Parameters from Chapter 9 of the book. See here for the full code repository.

• Height. Sample mean, random sampling, law of large numbers, bias, standard error, consistency, Chebyshev bound, convergence in probability, central limit theorem, convergence in distribution
• Gross domestic product. Sample mean, random sampling, law of large numbers
• COVID-19 prevalence. Sample proportion, random sampling, law of large numbers, bias, standard error, consistency, convergence in probability, central limit theorem, convergence in distribution
• Gambler’s paradox. Law of large numbers, sample mean
• The law of large numbers does not apply to the Cauchy distribution.
• Local economic activity. Law of large numbers, consistency of the sample mean, outliers
• Central limit theorem (discrete variables). Central limit theorem, convolution, sum of independent random variables
• Central limit theorem (continuous variables). Central limit theorem, convolution, sum of independent random variables
• Basketball strategy. Central limit theorem, Gaussian approximation to the binomial, Monte Carlo method
• Financial crisis. Central limit theorem, independence, Monte Carlo method
• Confidence intervals for the sample mean.
• Confidence intervals for precipitation. Confidence intervals, sample proportion, random sampling
• Bootstrap sample mean. The bootstrap, bootstrap standard error, sample mean
• Bootstrap Gaussian confidence intervals.
• Bootstrap percentile confidence intervals.
• Height and foot length. Correlation coefficient, sample correlation coefficient, Gaussian confidence intervals, the bootstrap, bootstrap percentile confidence intervals, Fisher’s transformation