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 continuous random variables, probability density functions, and distributions, with practical examples.
Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.
Covers the normal distribution, inferential statistics, probability, and the binomial distribution in the context of the 'Dishonest Gambler Problem'.
Explores modes of convergence in probability and statistics, illustrating concepts with examples and discussing the continuity theorem.
Covers fundamental concepts in probability and statistics, including distributions, properties, and expectations of random variables.
