Application of regression and time series models in finance; multiple regression; algebraic and geometric representation of least squares; inference methods – confidence intervals and hypothesis tests, ANOVA, prediction; model building and assessment; time series modeling; autoregressive AR(1) models – fitting, assessment and prediction; moving average smoothing, seasonal adjustment; non-stationarity and differencing. [Offered: F] Prereq: STAT 231 with a grade of 60% or STAT 241; Computing & Financial Management students and Mathematics/Accounting students who began F06 or later. Antireq: STAT 331, 361, 371, 372, 443
