Lectures related to Entropy and Disorder: Statistical Interpretation

Explores thermodynamic transformations of ideal gases, heat capacity, entropy, and adiabatic processes.

Explores conditional density, expectations, and independence of random variables with practical examples.

Explains how entropy measures uncertainty in a system based on possible outcomes.

Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.

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

Covers the basics of probability theory, random variables, joint probability, and inference.

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

Explores type I and type II errors, critical values, and confidence intervals in statistical significance.