Design of Experiments (DOE) is a tool to model a continuous output variable (Y) with continuous or discrete input/process variables (Xs). It describes a way to set up the eyperimental data collection (experimental plan) and to analyze the results from the conducted experiments (DOE analysis).

Main purposes
       – Screening – Reducing the number of Xs
       – Focusing – Verifying and quantifying significant X-Y relationships
       – Optimizing – Determining the best settings for the Xs  

Main outputs
       – Model – an equation of the form: 𝑌=𝑏0+𝑏1𝑋1+𝑏2𝑥2+𝑏3𝑥1𝑥2…
       – P-values of terms – how significant are the factors (Xs)?
       – R-sq, S unexplained – how much of the observed variation does the model explain, what portion remains unexplained?

The experimental plan is set up in a way
– To get as much information as possible from all the variables included in the design (each factor will be tested equally often on each level)
– This allows for checking for main effects and interactions of factors
       – Main effect: effect of one variable independent of others (e.g. for the Y “braking distance of a car”, a main effect may be the X “speed”)
       – Interaction effect: effect depends on the influence of two variables at the same time (e.g. for the Y “braking distance” an interaction of “tire type” and “street conditions” may be important: one type of tire is better on wet streets, another type of tire is better on dry streets – so you can’t generally say one type of tire is better, it depends)