A foundational study of probability and statistical inference. Topics include random variables and their distributions, estimation and testing of statistical hypothesis, sufficiency and completeness, the Cramer-Rao inequality, the Neyman-Pearson fundamendal lemma, nonparametric tests, correlation, and linear statistical models. In addition to mathematics students, this course is idea for graduate students in other disciplines that wish a more advanced knowledge of statistics. Prerequisite MAT 262, MAT 425, MAT 434 or equivalent.
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Last modified on Tuesday, January 12, 1999