In the last Blog, we explored the use of contour plots and other tools (such as a response optimizer) to help us quickly find solutions to our models. In this blog, we will look at the uncertainty in these predictions. We will also discuss model validation to ensure...

In the last Blog, we learned how to work with predictive models to find solutions that solve for desired responses. We used some basic algebra to solve for solutions and looked at the use of contour plots to quickly visualize many solutions at a glance. In this...

In the last Blog, we learned how to determine the coefficients of a predictive model for 2-level screening designs.It is more complex to determine model coefficients for multi-level experiments so for those, we rely on statistical methods software. In this blog, we...

In the last Blog, we learned how to determine which effects are statistically significant. This is an important step to develop the predictive model(s) because only the statistically significant factors and interactions belong in the model. If we include...

In the last Blog, we learned how to compute and graphically interpret both main effects and interaction effects. Eventually the statistically significant effects will be used to develop a predictive model. But how do we determine which effects are statistically...