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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.
Karen Benway ()
Dates: May 18 - August 14, 2015; Prereqs: MATH 020 or MATH 022
This course will be a study of how we can use probability to model random phenomena. Topics will include: - Properties of probability including conditional probability, independence and Bayes' Rule. - Discrete random variables and their probability distributions including Uniform, Hypergeometric, Binomial, Negative Binomial and Poisson distribution models. - Continuous random variables and their probability distributions including Uniform, Exponential, Gamma, Chi-square and Normal distribution models. - Bivariate distributions including joint, marginal and conditional probability distributions, sums of independent random variables and the bivariate normal distribution.
Two semesters of calculus are a prerequisite for STAT 151. We will use both differential and integral calculus.
Students will be evaluated based on: Weekly participation in Discussion Board 10% Weekly homework assignments 20% Proctored Midterm Exam 35% Proctored Final Exam 35%
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