MATHEMATICAL FOUNDATIONS OF BUSINESS ANALYTICS
Statistical inference and analyses based on models will be introduced and applied in a business context using a calculus-based focus. Topics covered include point estimation, confidence intervals, hypothesis testing, regression models, and time series models. Various methods for fitting a model to data will be explored, and uncertainty about parameter estimates will be quantified so decision makers have information about the quality of the estimates. Regression and time series models are commonly used in business analytics applications and will be used to analyze business data and make inferences and predictions.
Topics
- point estimation
- confidence intervals
- hypothesis testing
- regression models
- and time series models
Topics explicitly listed in official catalogue description
Prerequisites & course information
Requisites: MATH 331 , STAT/MATH 309 , or 431 Course Designation: Core GenEd - Mathematics & Quantitative Reasoning Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Apply discrete and continuous probability distributions to data encountered in business applications and evaluate the quality of parameter estimates. Audience: Undergraduate 2. Explain the basics of modeling using both regression and time series approaches. Audience: Undergraduate 3. Recognize the assumptions and limitations of the methods utilized in this course to aid businesses in making decisions under uncertainty. Audience: Undergraduate 4. Interpret statistical information for someone with a non/less technical background. Audience: Undergraduate 5. Analyze business data and construct models using appropriate statistical software. Audience: Undergraduate 6. Communicate and present the results of an analysis. Audience: Undergraduate Learning Outcomes: 1. Apply discrete and continuous probability distributions to data encountered in business applications and evaluate the quality of parameter estimates. Audience: Undergraduate 2. Explain the basics of modeling using both regression and time series approaches. Audience: Undergraduate 3. Recognize the assumptions and limitations of the methods utilized in this course to aid businesses in making decisions under uncertainty. Audience: Undergraduate 4. Interpret statistical information for someone with a non/less technical background. Audience: Undergraduate 5. Analyze business data and construct models using appropriate statistical software. Audience: Undergraduate 6. Communicate and present the results of an analysis. Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027