Introduction

Here in STAT410, we cover introduction to probability theory.

This includes probability, random variables, distribution functions, and much much more.

Notes (Outline)

An outline of notes for this course are given below.

• Combinatorics
• Probability
• Random Variables

Final Content

The following content should be known on the Final Exam.

• Markov’s Inequality
• Chebyshev’s Inequality (both cases $k=c\sigma$)
• Definition of convergence in probability and in distribution
• Central Limit Theorem (don’t need anything leading up to it, just the theorem itself, NO TAYLORS THEOREM NEEDED, NO CONTINUITY CORRECTION)
• Jensen’s Inequality

No need to know pdfs for discrete / continuous, expectation / variance for these.

Defo have 1,2,4,5 deeply memorized.

Larger part of the 1st half of the final is joint distributions.

Main Focus of the final will be on (5.3) transformations in 1 Variable, and on joint distributions, the next section. Before 5.3, know counting (1.1), Bayes’ Theorem (3.2), properties of E(X) and V(X). Know geometric series formula, series expansion for $e^x$.

Like only 25% of the final will be on material before 5.3 (transformations in 1 variable).