• Data capture.  Import needed data and display.

• Clean data.  Examine data.  Drop incomplete data.  Name data as it will be used for the project.  Fix fixable data.  Plot data iteratively to understand details.

• Prepare data.  Add variables for grouping and filtering.  Add medians and accumulators. continue to plot data iteratively.  

• Analyze data.  Produce plots of the data by factors deemed most important, overall and by specific factors.  

• Prepare secondary analyses.  From prior analyses, refine likely causal factors and more focused analyses.

• Select predictive analytics.  Develop predictive models for the processes.  This may involve models using linear and nonlinear mathematics and elements of machine learning and artificial intelligence.