Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Covers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.
Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.
Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.
