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Introduces the t-test for assessing categorical effects on quantitative outcomes, covering hypothesis testing, assumptions, and alternative tests.
Covers the concept of property testing using statistical methods.
Explores independence and covariance between random variables, discussing their implications and calculation methods.
Covers the Metropolis-Hastings algorithm and gradient-based approaches for biasing searches towards higher likelihood values.
Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.
Covers estimation, shrinkage, and penalization in statistics for data science, emphasizing the importance of balancing bias and variance in model estimation.
