What is Experimental Design?¶

In an experiment, we deliberately change one or more process variables (or factors) in order to observe the effect the changes have on one or more response variables. The (statistical) design of experiments (DOE) is an efficient procedure for planning experiments so that the data obtained can be analyzed to yield valid and objective conclusions.

DOE begins with determining the objectives of an experiment and selecting the process factors for the study. An Experimental Design is the laying out of a detailed experimental plan in advance of doing the experiment. Well chosen experimental designs maximize the amount of information that can be obtained for a given amount of experimental effort.

The statistical theory underlying DOE generally begins with the concept of process models.

Process Models for DOE¶

It is common to begin with a process model of the black box type, with several discrete or continuous input factors that can be controlled--that is, varied at will by the experimenter--and one or more measured output responses. The output responses are assumed continuous. Experimental data are used to derive an empirical (approximation) model linking the outputs and inputs. These empirical models generally contain first and second-order terms.

Often the experiment has to account for a number of uncontrolled factors that may be discrete, such as different machines or operators, and/or continuous such as ambient temperature or humidity. Figure 1 illustrates this situation.

The most common empirical models fit to the experimental data take either a linear form or quadratic form.

A linear model with two factors, \(X_1\) and \(X_2\), can be written as $$ Y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \beta_{12} X_1 X_2 + \text{experimental error} $$

Here, \(Y\) is the response for given levels of the main effects \(X_1\) and \(X_2\) and the \(X_1X_2\) term is included to account for a possible interaction effect between \(X_1\) and \(X_2\). The constant \(\beta_0\) is the response of \(Y\) when both main effects are 0.

For a more complicated example, a linear model with three factors \(X_1\), \(X_2\), \(X_3\) and one response, \(Y\), would look like (if all possible terms were included in the model) $$ Y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \beta_3 X_3 + \beta_{12} X_1 X_2 + \beta_{13} X_1 X_3 + \beta_{23} X_2 X_3 + \beta_{123} X_1 X_2 X_3 \newline + \text{ experimental error} $$

The three terms with single \(X's\) are the main effects. For three factors there are \(k(k-1)/2 = 3*2/2 = 3\) two-way interaction terms and 1 three-way interaction term (often omitted for simplicity). When the experimental data are analyzed, all unknown \(\beta\) parameters are estimated and the coefficients of the \(X\) terms are tested to see which ones are significantly different from 0.

A second-order (quadratic) model (typically used in response surface DOE's when curvature is suspected) does not include the three-way interaction term but adds three squared terms to the linear model, namely $$ \beta_{11} X_1^2 + \beta_{22} X_2^2 + \beta_{33} X_3^2 $$