First, we describe the parameters of random variables, i.e., quantiles, media, raw moments, central moments, absolute moments, variance, skewness, and kurtosis. These parameters appear in a series of probability inequalities, such as the Schwartz inequality, Jensen inequality, Holder inequality, and Minkowski inequality. A number of versions of Chebyshev inequality are also discussed. The chapter further extends to numerical parameters of multidimensional distributions (including multidimensional Gaussian distribution), and introduces the concept of copulas, i.e., distributions on the cube \([-1, 1]^n\) , where all one-dimensional projections are uniform. Chapter VI. The chapter introduces a very important tool in probability theory: Characteristic functions, their properties, and relations between characteristic functions (and their differentiability) and cumulative distribution functions, and moments of random variables and vectors. After defining the weak convergence of distributions, i.e., the convergence in distribution of the corresponding random elements, this convergence is described in terms of characteristic functions. The main result in the last section is the Lévy-Cramer Theorem.