Dex scatter plots are typically used in conjunction with the dex mean plot and the dex standard deviation plot. The dex mean plot replaces the raw response values with mean response values while the dex standard deviation plot replaces the raw response values with the standard deviation of the response values. There is value in generating all 3 of these plots. The dex mean and standard deviation plots are useful in that the summary measures of location and spread stand out (they can sometimes get lost with the raw plot). However, the raw data points can reveal subtleties, such as the prescene of outliers, that might get lost with the summary statistics.

This sample dex scatter plot shows that:

1. there do not appear to be any notable outliers;
2. the levels of factors 2 and 4 show distinct location differences; and
3. the levels of factor 1 show distinct scale differences.

• Vertical axis: Value of the response variable
• Horizontal axis: Factor variable

1. Which factors are important with respect to location and scale?
2. Are there notable outliers?

Dex scatter plots were designed primarily for analyzing designed experiments. However, they are useful for any type of multi-factor data (i.e., a response variable with 2 or more factor variables having a small number of distinct levels) whether or not the data were generated from a designed experiment.

Specifically, if there are k factors, we create a matrix of plots with k rows and k columns. On the diagonal, the plot is simply a dex scatter plot with a single factor. For the off-diagonal plots, we multiply the values of X_i and X_j. For the common 2-level designs (i.e., each factor has two levels) the values are typically coded as -1 and 1, so the multiplied values are also -1 and 1. We then generate a dex scatter plot for this interaction variable. This plot is called a dex interaction effects plot and an example is shown below.