Forecasting methods in business introduces students to quantitative techniques that use historical data to make predictions. The course begins with an overview of basic statistical concepts, time series regression analysis, and model building and residual analysis. Emphasis is placed on obtaining point forecasts, prediction intervals for mean values, prediction intervals for individual values, detecting autocorrelation, and assessing forecast error. Students will also learn how to model trend, cyclical, and seasonal variation using polynomial, trigonometric, and growth curve regression models. Forecasting using additive decomposition, multiplicative decomposition, simple exponential smoothing, trend-corrected exponential smoothing, and Holt-Winters methods will also be covered. Finally, students will use Box-Jenkins analysis to identify, estimate, and forecast time series models.