Foundations of probability, conditioning, and independence. Business, computing, biological, engineering reliability, and quality control applications. Classical discrete and continuous models. Pseudo-random number generation. Prerequisites: MATH 020 or MATH 022 or MATH 023.
Dates: May 20 - July 12, 2019
Two semesters of calculus are a prerequisite for this course. In order to understand real world phenomena, it is necessary to account for the role of randomness. Probability provides a framework to understand and model randomness. Topics will include: axioms of probability, basic combinatorics, conditional probability and independence, distributions of random variables, mathematical expectation, and functions of random variables.
Textbook: Probability & Statistical Inference by Hogg, Tanis & Zimmerman. (Don't get the "Global Edition" because the problems are different from the U.S. version!) A printed copy is rather expensive, but the e-book is a less expensive option and is sufficient.
Grades will be determined based on the following criteria: Discussion Board Participation 10% Quizzes 40% Exams 50%
Online Course (View Campus Map)