| 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) |
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