STSCI 4520 - Statistical Computing

(SDS-AS) (OPHLS-AG)

Spring. 4 credits. Student option grading.

Prerequisite: BTRY 3080 or

, enrollment in MATH 2220 and MATH 2240 or equivalents. Previous programming experience is recommended. Co-meets with STSCI 5520.

J. Guinness.

This course is designed to provide students with an introduction to statistical computing. The class will cover the basics of programming; numerical methods for optimization and linear algebra and their application to statistical estimation, generating random variables, bootstrap, jackknife and permutation methods, Markov Chain Monte Carlo methods, Bayesian inference and computing with latent variables.

Outcome 1: Students will be able to enter, manipulate and plot data and run basic statistical analyses in R.

Outcome 2: Students will be able to implement estimators for non-standard statistical problems in R.

Outcome 3: Students will be able to simulate random variables and random experiments in R.

Outcome 4: Students will be able to design and implement Monte Carlo methods to evaluate integrals and perform simulations.

Outcome 5: Students will be able to design and conduct appropriate resampling methods to estimate sampling variance for statistical estimates.