If your spreadsheet model includes a regression equation and you are not explicitly modeling the associated error, for a linear model you are implicitly assuming that there is no error. On average that’s a safe assumption; it’s individual occurrences however that can make this risky. After a brief review of regression fundamentals (yeah, calculus), this webinar will use applications from multiple industries to show how @RISK can be used to model a regression equation’s error term for both linear and nonlinear (intrinsically linear) models. Along the way you will improve your skill working with the normal distribution and add the lognormal distribution to your repertoire.