STA 13 or 32 or 100 : Fall, Winter, Spring . endstream MAT 108 is recommended. Program in Statistics - Biostatistics Track, Random experiments, sample spaces, events, Independence, conditional probability, Bayes Theorem, Covariance and conditional expectation for discrete random variables, Special distributions and models, with applications, Discrete distributions including binomial, poisson, geometric, negative binomial and hypergeometric, Continuous distributions including normal, exponential, gamma, uniform, Sums of independant binomial, poisson, normal and gamma random variables, Central limit theorem and law of large numbers, Approximations for certain discrete random variables, Minimum variance unbiased estimation, Cramer-Rao inequality, Confidence intervals for means, proportions and variances. ), Prospective Transfer Students-Data Science, Ph.D. Double Major MS Admissions; Ph.D. Illustrative reading:Introduction to Probability, G.G. Title: Mathematical Statistics I A primary emphasis will be on understanding the methodologies through numerical simulations and analysis of real-world data. STA 131A Introduction to Probability Theory. Course Description: Topics in asymptotic theory of statistics chosen from weak convergence, contiguity, empirical processes, Edgeworth expansion, and semiparametric inference. Summary of Course Content: The minor is designed to provide students in other disciplines with opportunities for exposure and skill development in advanced statistical methods. Course Description: First part of three-quarter sequence on mathematical statistics. STA 231B: Mathematical Statistics II | UC Davis Department of Statistics University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. ), Statistics: Computational Statistics Track (B.S. Course Description: Advanced programming and data manipulation in R. Principles of data visualization. One-way random effects model. Math 21D, Winter 2020 - UC Davis Course Description: Basic probability, densities and distributions, mean, variance, covariance, Chebyshev's inequality, some special distributions, sampling distributions, central limit theorem and law of large numbers, point estimation, some methods of estimation, interval estimation, confidence intervals for certain quantities, computing sample sizes.