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.
Prereqs enforced by the system: MATH 020 or MATH 022 or MATH 023; Open to Degree and Continuing Education students
We will discuss a variety of theory and applications including topics in combinatorics, conditional probability, random variables, mathematical expectation, limit theorems, and statistics. There will be a mixture of in-class group work, lecture (pre-class materials with a recap during class), discussion to highlight particular topics. Key topics are: *Properties of probability (conditional probability, independent events, Bayes' rule) *Discrete distributions (Bernoulli, binomial, geometric, hypergeometric, negative binomial, Poisson) *Continuous distributions (uniform, exponential, gamma, chi-square, normal) *Bivariate distributions (conditional distributions, sums of independent random variables) *Distributions of functions of random variables *The Central Limit Theorem and its applications. The required textbook for the course will be "Probability and Statistical Inference" by Hogg, Tanis, and Zimmerman, 10th edition (ISBN-10: 013518939X; ISBN-13: 978-0135189399).
Students are expected to review pre-class materials, attend class meetings, participate in in-class group work, complete assignments, and complete exams. Students should expect between two and four hours of work outside of class for every hour of class time. Homework assignments will be posted and completed on Blackboard. Late assignments will not be accepted.
Final grades will be determined by assignments, exams, and class participation.
Votey Bldg 205 (View Campus Map)
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