3.
Production
Process Characterization
3.4.
Data Analysis for PPC
3.4.2.

Exploring Relationships


The first analysis of our data is exploration

Once we have a data file created in the desired
format, checked the data integrity, and have estimated the summary statistics
on the response variables, the next step is to start exploring the data
and to try to understand the underlying structure. The most useful
tools will be various forms of the basic scatter plot and box plot.


These techniques will allow pairwise explorations for
examining relationships between any pair of response variables, any pair
of explanatory and response variables, or a response variable as a function
of any two explanatory variables. Beyond three dimensions we are pretty
much limited by our human frailties at visualization.

Graph everything that makes sense

In this exploratory phase, the key is to graph everything that makes
sense to graph. These pictures will not only reveal any additional quality
problems with the data but will also reveal influential data points and
will guide the subsequent modeling activities.

Graph responses, then explanatory versus response, then
conditional plots

The order that generally proves most effective for data analysis is
to first graph all of the responses against each other
in a pairwise fashion. Then we graph responses against the
explanatory variables. This will give an indication of the main
factors that have an effect on response variables. Finally, we
graph response variables, conditioned on the levels of explanatory
factors. This is what reveals interactions between explanatory
variables. We will use nested
boxplots and
block plots to visualize
interactions.
