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Using Spline Regression In Interpreting Demographic Data

I have been using the outputs from Spline Regression to facillitate analysis of demographic data (specifically Department of Labor Quarterly Employment data).  I have data from 1992Q1 to 2014Q1 and use Spline Regression to get fitted values for each quarter with predictors being the year/quarter, Year/quarter multiplied by a dummy variable for each of the 4 US Presidents, and a dummy variable for each president. 
So I can compare results across various groupings by geographic, and other levels as well as the BLS aggregation level. I can analyze raw data or have the values to be fitted indexed to 1992Q1.
I use the default settings for Spline and it builds the best fit including where the node periods for each spline section.  To help interpret the results, though, I use the output to compare the actual vs. fitted values (e.g. employment Level) and then look at the changes by quarter. 
With the spline regression building the best model with optimal line segments, the results make it possible to see how employment progress or regress correletat with with presidential terms of office or specific impacts of economic recessions on employment data.

I can supply an example of the process, if anyone is interested.

I'd appreciate any comments and/or suggestions to improve the process or interpret the results.  

2 Comments
Alteryx Certified Partner

Hi,  Can you share the example used for building Spline model in alteryx with workflow. Thanks

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