Course Catalog Description
This is a basic statistics course for undergraduate students. It is a study of the acquisition, presentation, analysis, and interpretation of data. Topics include descriptive statistics and statistical graphics, experiments vs. observational studies, elementary probability, random variables and distributions, sampling distributions of statistics, confidence intervals, hypothesis testing for means and proportions, correlation, linear regression, and an introduction to ANOVA.
The prerequisites are the satisfaction of the Mathematics Proficiency requirement.
- Teacher: Ole Forsberg
Course Catalog Description
STAT 225: Linear Models and Statistical Software
This course develops further the ideas and techniques that were introduced in STAT 200 relative to regression modeling and experimental design, understood as instances of a matrix linear model. In addition, the student becomes familiar with at least one leading statistical package for performing the intensive calculations necessary to analyze data. Topics include linear, non-linear, and multiple regression, model-building with both quantitative and qualitative variables, model-checking, logistic regression, experimental design principles, ANOVA for one-, two-, and multiple factor experiments, and multiple comparisons.
Prerequisites: STAT 200, MATH 145 or 151, and MATH 185.
Cross-listed as MATH 225
- Teacher: Ole Forsberg
Course Catalog Description
MATH 322: Mathematical Statistics II
A rigorous study of the theory of statistics with attention to its applications. Point and interval estimation, hypothesis testing, regression and correlation, goodness-of-fit testing, and analysis of variance.
Prerequisite: MATH 321
- Teacher: Ole Forsberg
Course Catalog Description
STAT 312: Data Mining and Statistical Programming
A rigorous exploration of statistical methods designed to glean information from a data set. Techniques include categorical analysis, clustering, trees and forests, dimensionality reduction, and outlier detection. Further topics include graphical and statistical methods for exploring data, as well as evaluating statistical methods. The Python programming language will be used.
Prerequisites: STAT 200 (plus another STAT course), CS 142, MATH 145 or 152, and MATH 185.
Cross-listed as CS 312
- Teacher: Ole Forsberg
Course Catalog Description
This is a course designed to help the typical person in the United States (and beyond) properly interpret and understand the complexities of polls, especially political polls during election seasons. This interpretation necessitates an understanding of such terms as confidence intervals, credible intervals, and sampling effects, as well as an understanding of the electoral system.
There are no prerequisites for this course.
- Teacher: Ole Forsberg