Decision Science (DS)
DS 2305 Business Statistics
Descriptive and inferential statistical techniques for business and economic decision-making. Topics include the collection, description, analysis, and summarization of data; probability; discrete and continuous random variables; the binomial and normal distributions; sampling distributions; tests of hypotheses; estimation and confidence intervals; linear regression; and correlation analysis. Statistical software is used to analyze data throughout the course.
DS 2310 Business Statistics I
An introductory study of statistical methods as applied to business and economic problems. Topical coverage includes descriptive statistics, set theory, probability theory, random variables, probability distributions, the normal distribution, sampling methods, sampling distributions, statistical estimation, hypothesis testing, and simple linear regression and correlation analysis.
Prerequisites: MATH 1325 with a grade of "C" or better.
DS 6320 Multivariate Statistics
The objective of this course is to introduce powerful methods for understanding and obtaining managerial insight from multivariate data. Multivariate methods studied in the course include multiple regression, confounding and interaction in regressions, dummy variables, logistic regression and Poisson regression. The class will also examine the data, interdependent techniques, and dependent techniques.
Prerequisites: Consent of the instructor and the Graduate Advisor.