This course will enhance MBA students' analytical skills and their ability to make informed and data- driven decisions based on accurate predictions in today's complex business environment. A wide range of alternative forecasting techniques will be discussed and critically reviewed to identify market opportunities, demand planning, and evaluate risk factors. Students will apply real-world data using alternative computer packages to create scenarios for business decision makers. Topics include exponential smoothing and Error Trend Seasonality (ETS), regression, times series decomposition, Box- Jenkins methods, and generalized autoregressive conditional heteroskedasticity (GARCH) models. All these techniques will be practiced through a hands-on process using mini-case studies. Prerequisite: BUSN 5010 (min. grade B-) and admission to the MBA program or approval by the degree committee.
This course will enhance MBA students' analytical skills and their ability to make informed and data- driven decisions based on accurate predictions in today's complex business environment. A wide range of alternative forecasting techniques will be discussed and critically reviewed to identify market opportunities, demand planning, and evaluate risk factors. Students will apply real-world data using alternative computer packages to create scenarios for business decision makers. Topics include exponential smoothing and Error Trend Seasonality (ETS), regression, times series decomposition, Box- Jenkins methods, and generalized autoregressive conditional heteroskedasticity (GARCH) models. All these techniques will be practiced through a hands-on process using mini-case studies. Prerequisite: BUSN 5010 (min. grade B-) and admission to the MBA program or approval by the degree committee.
