Chapter 3. Learning About Models
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
Models and experiments
Models can take many forms: a verbal description, set of mathematical equations, or segment of computer code. In this book, we are interested in a specific kind of model, probabilistic or statistical model, which...
