STAT 302 – Introduction to Probability

First off, I would just like to say that this was the hardest course I have ever taken. Statistics 302 – Introduction to Probability covered so much material and drew concepts from calculus 1,2,3 and STAT 200. I found myself studying for not only the course itself, but I also had to review integration, multi-variable calculus, and introductory statistical analysis techniques. I truly do think that the material I learned will be useful in the future. Often times I would relate what I was learning to various real life situations.

Just a short list (in no particular order) of what was covered in this course:

- Advanced combinatorics / permutations and combinations (probably one of the hardest chapters)
- Probability laws which was almost the same as set theory (union, intersection, partition, commutative associative distributive and DeMorgan’s laws, complement, subset, disjoint)
- Conditional probability (Baye’s formula, odds, independence of events, conditional independence)
- Discrete random variables (probability mass function, cumulative distribution function, expectation, variance/standard deviation)
- Common discrete random variables: Bernoulli, binomial, geometric, negative binomial, Poisson, hypergeometric
- Continuous random variables (probability density function, cumulative distribution function, gamma/uniform/normal/exponential distributions)
- Joint probability (this chapter was also really difficult and covered so much)
- Markov and Chebyshev’s inequality
- Moment generating functions