Spring 2024
Meeting:
MW 8:30am - 9:50am / MLR 301
SLN:
16957
Section Type:
Lecture
Joint Sections:
STAT 394 A
Instructor:
Kelly Kang
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Catalog 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. Course overlaps with: E E 391; STMATH 392; and TMATH 393. Prerequisite: either a minimum grade of 2.0 in MATH 126, or a minimum grade of 2.0 in MATH 136. Offered: jointly with STAT 394; AWSpS.
GE Requirements Met:
Natural Sciences (NSc)
Credits:
3.0
Status:
Active
Last updated:
December 6, 2024 - 5:40 pm