Lectures related to Parametric statistics

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Covers nonparametric statistics, kernel density estimation, Bayesian principles, and posterior distribution summarization.

Covers the normal distribution, including its properties and calculations.