ST 310 - Statistical Computing

(3.00 cr.)

Prerequisite: ST 210 or ST 265 or EC 220 , or written permission of the department chair. Reviews a number of statistics topics as a vehicle for introducing students to statistical computing and programming using SAS and R for graphical and statistical analysis of data. Statistics topics include graphical and numerical descriptive statistics, probability distributions, one and two sample tests and confidence intervals, simple linear regression, and chi-square tests. SAS topics include data management, manipulation, cleaning, macros, and matrix computations. Topics in R include data frames, functions, objects, flow control, input and output, matrix computations, and the use of R packages. Lastly, this course also includes an introduction to the resampling and bootstrap approaches to statistical inference. Required for statistics and data science majors. DS/IDS (Spring only)