This lecture covers the calculation of expectation and variance for different types of random variables, including discrete and continuous ones. It explains how to compute the expectation and variance using probability density functions and provides examples with T(k, X) and N(μ, σ²) distributions. The lecture also introduces the Cauchy distribution, a special case without expectation, and discusses the support, range, and invertibility of cumulative density functions.