Regression and Forecasting Models for Business Applications

This course provides a thorough review of regression and forecasting approaches as applied to business applications. Among the topics covered are residual and influence analysis; multiple regression models, including selection criteria, curvilinear regression, dummy variables, and logistic regression; and time series models, including the classical multiplicative model, moving averages, exponential smoothing, and the autoregressive model.

Fall, Spring, Summer

023422