Courses
Quantitative Business Analysis Courses
QBA201 Managerial Statistics
[3–0, 3 cr.]
This course covers the basic concepts in descriptive and inferential statistics relevant to managerial decision making. Topics include data analysis, probability, random variables, sampling distributions, estimation, hypothesis testing and regression. Examples and case studies are drawn from finance, marketing, and management to aid understanding of the statistical techniques and assist in their implementation. Extensive use of statistical software package tools is made for representing and analyzing data.
Prerequisite: None
Equivalent: BUS210 Business Statistics
QBA301 Intermediate Managerial Statistics
[3–0, 3 cr.]
This course addresses more advanced topics in statistics for business students.
Prerequisites: BUS210.
QBA730 Business Analytics for Executives
[1.5–0, 1.5 cr.]
This course covers the statistical techniques and concepts that a manager uses in making decisions. Topics include problem formulation, sampling techniques, data collection and analysis; statistical inference, including estimation and sample size determination; and regression and correlation analysis.
QBA810O Core Business Analytics & Statistics
[3–0, 3 cr.]
Covers statistical and business analytics tools useful for making effective managerial decisions in a disorganized and uncertain environment in all functional areas of business. Students learn the essential statistical topics of description, probability, inference and regression, and how to apply them using Microsoft Excel. They learn how to choose appropriate statistical methods in realistic business contexts and how to interpret and effectively communicate results. Students also learn how to use data visualization tools, pivot tables and charts, data tables, optimization models and Monte Carlo simulation.QBA851 Quantitative Methods in Business
[3–0, 3 cr.]
This course is an introduction to the application of mathematical techniques in business decision-making, emphasizing practical usage in management situations. Topics include linear programming, transportation problems, network planning, queuing theory, regression analysis, and modeling techniques.