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