Main effects plots show how each factor affects the response characteristic. A main effect is present when different levels of a factor affect the characteristic differently. For a factor with two levels, you may find that one level increases the mean compared to the other level. This difference is a main effect.

Minitab creates the main effects plot by plotting the characteristic average for each factor level. These averages are the same as those displayed in the response table. A line connects the points for each factor. Look at the line to determine whether or not a main effect is present for a factor.

·    When the line is horizontal (parallel to the x-axis), then there is no main effect present. Each level of the factor affects the characteristic in the same way and the characteristic average is the same across all factor levels.

·    When the line is not horizontal (parallel to the x-axis), then there is a main effect present. Different levels of the factor affect the characteristic differently. The greater the difference in the vertical position of the plotted points (the more the line is not parallel to the X-axis), the greater the magnitude of the main effect.

By comparing the slopes of the lines, you can compare the relative magnitude of the factor effects.

For the basil data, the main effects plot for the signal-to-noise ratio is shown. The plots indicate the following:

·    Fertilizer has the greatest effect on signal-to-noise ratio. Runs with Fertilizer 2 had much higher signal-to-noise ratios than runs with Fertilizer 1.

·    Spraying had virtually no effect on signal-to-noise ratio, which is shown by the almost flat line.