Statistics 110 - Introduction to Probability
Instructor: Mark Irwin
A comprehensive introduction to calculus based probability. Basics: sample space, conditional probability, Bayes Theorem. Univariate distributions: mass functions and density, expectation and variance, binomial, Poisson, normal, and gamma distributions. Multivariate distributions: joint and conditional distribution, independence, transformation, multivariate normal and related distributions. Limit laws: probability inequalities, law of large numbers, central limit theorem. Monte Carlo (simulation) methods. Markov chains: transition probability, stationary distribution and convergence.
The final exam will be held in Science Center 222 on Wednesday, August 16th at 9:00.
This 3 hour exam will be comprehensive, though will be weighted a bit towards material covered after the midterm. You may bring 2 pages of notes to the exam and a calculator. As with earlier exams, any tables that might be needed will be supplied with the exam.
Exam Week Office Hours
Monday: 1:00 - 3:00