## Course Summary

As the course title suggests, the content of Math 220 is divided into two related components. First, we learn the basics of probability theory. Roughly speaking, probability is the science of determining the likelihood of a certain event. This includes some fundamental concepts (e.g., conditional probability, independent events) and theorems (e.g., Bayes's Theorem), but also requires us to develop the ability to carefully enumerate possibilities. We will explore some common discrete and continuous probability distributions, and see how they are related to the so-called normal distribution via the Central Limit Theorem.

After discussing probability, we will turn our attention to statistics. Whereas the former asks us to determine the likelihood of an outcome based on an assumed model of a given random process, statistics asks us to determine a reasonable model for a random process based on observed outcomes of that process. For instance, if we roll a pair of fair dice, probability theory tells us that it's six times more likely that we would see a total of 7 on the dice than that we'd see a total of 12. If, on the other hand, we're given two dice and roll them 100 times, and we find that we get a total of 12 for 50 of those rolls, then we can reasonably infer that the dice aren't fair (and given more information, we could describe more explicitly the bias in the dice). We will cover a handful of statistical topics: sampling, estimation, confidence intervals, and hypothesis testing.

## Course Instructor

The professor for this class is Andy Schultz. His office is on the third floor of the Science Center, room S352. You are highly encouraged to attend office hours, and you never need an appointment to do so. If these office hours don't fit with your schedule, contact the instructor so that he can either adjust when ``official" office hours are held or set up an appointment to help you outside of office hours.