Statistics: Exploratory Data Analysis

Related lectures (30)

Page 2 of 3

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Explores variance, covariance, and practical applications in statistics and probability.

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.

Explores estimating parameters through confidence intervals in linear regression and statistics.

Discusses vectors of random variables and empirical distributions, including their properties and significance in statistics.

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.