Diagnostics

Residuals = observed – model predicted data. They represent the noise left over after the systematic model effects are removed. Use the Diagnostics Tool to display these residuals in simple plots to check your model assumption

• If the residuals lie roughly on a straight line, then the noise is approx. Normally distributed
• If the residuals are equally spread out across the plot – the prediction range – & around zero then the noise is constant & centred around zero (no noise). The tram lines help identify outliers  or large residual values
• If there is a trend in the residuals vs. run order, this would indicate something not in the model was changing over time. E.g., a downward trend may indicate degrading starting material
• If the model fits well to the data, the predicted and actual should correspond closely and approximately lie on a straight line
• If the residuals are not normally and consistently spread (e.g. fan out/worsen as the predictions get bigger) use Box-Cox plot to help you choose a transform to try out (i.e. go back to Transform)
• If the effects of the factors have been adequately captured by the model, then there will be no remaining systematic relationship between residuals versus factor settings and the variation across the factor settings will be consistently spread