Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Explores continuous random variables, density functions, joint variables, independence, and conditional densities.
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Covers the multinomial distribution, joint density, marginal distribution, and conditional distribution.
Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
