data-modeling.Rmd

# Data Modeling {.unnumbered}


In this next section, we will discuss how to start modeling your cleaned data. This is again loosely broken into several sections.

We will start by discussing the big picture view of how modeling works and learn key conceptual topics like populations vs samples, probability vs statistics, parameters vs estimates, etc.

The, we will cover some foundational probability principles that are necessary to understanding later modeling steps, such as discrete and continuous random variables, distributions, PDF/PMFs vs CDFs, expectation and variance, and so on.

Next, we will learn about arguably the two most important and versatile random variables: normal & binomial, which form the basis of two general categories of models we will discuss later (means vs proportions infereence).

Finally, we will conclude by introducing the basic framework of hypothesis tests & confidence intervals, which we will explore and apply further in the following section.