If your cost model includes an Ordinary Least Squares (OLS)-based Cost Estimating Relationship (CER) and you are not explicitly modeling the associated error, for a linear regression 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 OLS regression fundamentals (yeah, calculus), this webinar will show how @RISK can be used to model a regression equation’s error term for both linear and nonlinear (intrinsically linear) CERs. Along the way you will improve your skill working with the normal distribution and, if it’s not already part of it, add the lognormal distribution to your repertoire.