Catalog Course Description

This course teaches a broad range of statistical methods that are used to solve data problems. Topics include group comparisons and ANOVA, standard parametric statistical models, multivariate data visualization, multiple linear regression, logistic regression and classification, regression trees and random forests. An important focus of the course is on statistical computing and reproducible statistical analysis. The course and lab include hands-on experience in analyzing real world data from the social, life, and physical sciences. The R statistical language is used.

Fall Term

2 hours of Instructional experiences requiring special laboratory equipment and facilities per week and 3 hours of Instructor presentation of course materials per week and 7 hours of Outside Work Hours per week.

Spring Term

2 hours of Instructional experiences requiring special laboratory equipment and facilities per week and 3 hours of Instructor presentation of course materials per week and 7 hours of Outside Work Hours per week.

Statistics 131A