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What are the key differences between Multiple Regression using historical data and Multiple Regression based on Experimental Data (DOE)? Using historical data : you can see how the actual data fits the regression line you can see outliers and unusual observations to investigate or collect more data you can confirm if residuals are random and follow normal distribution DOE 1. models a regression line based upon experimental data - may not reflect all influences - noise, environmental, and control factors as in real data 2. models are only as good as the SME /teams that provide insight to the scope, factors, boundaries, and interactions What are the advantages of one over the other, if at all? using DOE can model and understand interactions with smaller runs/ replications ; can screen for critical X's and then evaluate interactions and provide model equation that cover more factors and levels more efficiently and effectively.