STAT 394

Probability I
3.0 credits

Course Description

Axiomatic definitions of probability; random variables; conditional probability and Bayes' theorem; expectations and variance; named distributions: binomial, geometric, Poisson, uniform (discrete and continuous), normal and exponential; normal and Poisson approximations to binomial. Transformations of a single random variable. Markov and Chebyshev's inequality. Weak law of large numbers for finite variance. Prerequisite: either a minimum grade of 2.0 in MATH 126, or a minimum grade of 2.0 in MATH 136. Offered: jointly with MATH 394; AWS.

Current Time Schedule

Winter, 2021

SLN/Section Time Location Instructor
20448 A

M W F 1:30PM - 2:20PM

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Vincent Roulet