STT 4840 - Regression and Time Series Forecasting (3)

When Offered: Fall
Introduction to regression and time series forecasting models applied to problems in economics, business and the social sciences with emphasis on the use of computer technology. Topics include least squares parameter estimation, simple and multiple linear regression models, trend and seasonal regression models, seasonal and non-seasonal ARIMA models, model assumptions diagnostics, variable selection, model evaluation and monitoring, smoothing techniques and dealing with non-stationarity.
Prerequisites: MAT 2240, STT 3250, and STT 3850 or permission of the instructor. (ND Prerequisite: passing the math placement test or successful completion of MAT 0010.)