Leif Peterson

Topics include basic probability, descriptive statistics, probability distributions, confidence intervals, significance testing, simple linear regression and correlation, association between categorized variables.

An introduction to statistics for Biosciences with emphasis on statistical models and data analysis techniques. Computer-assisted data analysis and examples, are explored in laboratory sessions. Topics include descriptive statistics, correlation and regression, categorical data analysis, statistical inference through confidence intervals and significance testing, rates, and proportions. Real-world examples are emphasized. Cross-list: STAT 305. Recommended Prerequisite(s): MATH 212 or MATH 222

This multidisciplinary course for non-STAT majors will address modern applied multivariate statistical methods used in business, economics, engineering, biomedicine, and the environmental and social sciences. Topics include covariance and correlation matrices, multivariate analysis of variance, unsupervised class discovery, cluster validity, supervised class prediction, classifier ensemble diversity. Recommended Prerequisite(s): Introductory statistics course, such STAT 280 or STAT 305 or STAT 310 or STAT 315.