Traditional methods for doing driver analysis (using correlation analysis or regression analysis) suffer from a well-known flaw, particularly from the fact that collinearity degrades both of them badly. After showing the evidence for not doing simple correlation or regression analysis, we show how each of them can be done better. And after considering the red herring that is factor analysis, we’ll go on to cover three modern methods (averaging over orderings, Johnson’s relative importance weights and random forests) that enable an analyst to conduct driver analysis in ways that accommodate collinearity. In addition, we’ll provide advice for how to run these new models with free software, including R code for all methods we discuss and other free web-based tools for those who don’t use R.
This webinar will take place on Wednesday, August 19, 2020 from 9:00 AM to 10:00 AM MDT. No prior experience is necessary. 24 hours after the session has concluded the recording and materials will be found below.