Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.
Covers the process of making pizza, sampling, averages, dispersion, residuals, and normal distribution.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Covers concentration inequalities and sampling methods for estimating unknown distributions, with a focus on population infection rates.
Covers the normal distribution, inferential statistics, probability, and the binomial distribution in the context of the 'Dishonest Gambler Problem'.
