In the most generic sense, a model is an approximate description of a portion of reality. Models are essential to science and, in fact, any area of knowledge: it is only possible to comprehend the world by concentrating on a small part of it at a time and making suitable simplifications.
In this chapter, we will discuss the following topics:
Using basic models in data analysis
Using the cumulative distribution function and probability density function to characterize a variable
Using the preceding functions and various tools to make point estimates and generating random numbers with a certain distribution
Discussing examples of discrete and continuous random variables and an overview of multivariate distributions