Effects Plot
The following describes the Effects Plot for analyzing Design of Experiments results with MLR.
Effects Plot
Usage
Effects plots are used in the evaluation of factorial designs to identify which experimental factors are important, including interactions, and to get a feel for the magnitude of the effect verses experimental error. Clicking on the 'DOE Effects Plot' menu button will open a window where you select which main effect or interaction effect you wish to view. Selecting an effect and clicking 'OK' opens the Effects Plot. You are also given the opportunity to change the default confidence level value (0.05) used in calculating Fisher's Least Significant Difference (LSD) which is included in the plots.
Interpretation
Main Effects
Main Effects plots show how the mean response of a factor varies over its levels. The levels of this factor are marked on the x-axis. Data points are plotted with y-value giving the mean for the factor at that factor level. This figure shows the main effect of factor A, showing the mean response increases with factor A level.
The effects plots may also show 'error bars' about each plotted point indicating the magnitude of Fisher's LSD. Data points must be farther apart vertically than the LSD size to be considered significantly different (at the confidence level used).
Interaction Effects
An interaction effect refers to the effect of a second factor on the main effect of a first factor. The interaction of factor A and factor B can be presented by showing the main effect of factor A for each level of factor B. If factor B has two levels then there will be two lines plotted, one showing the main effect of factor A when only considering experiments where factor B level is low, and one showing the main effect of factor A when only considering experiments where factor B is high. An interaction effect is significant if there is a change in the main effect of one factor over levels of the second. The figure below shows that there is a significant interaction between factors A and B. The blue line shows the mean response increases with factor A level when factor B level is low while the green line shows that the mean response decreases with factor A level when factor B level is high.
