Hello all,
Can someone help me understand why if I use the Pearson correlation tool vs the Association Analysis tool on the same data set with the same selected variables how can I interpret the output and explain the variables simply to a non technical/non mathematical business audience?
- If i am trying to predict revenue on store, I assume I would want to use the association analysis tool, then select the most important variables showcased by p value? Or is this not the best way to interpret?
- It appears as though Pearson correlation tool is outputting the "Association Measure" while the association analysis tool is outputing the "Association Measure" and the "p-value"
- Is this accurate from how the tools work?
- If I was explaining this to a non technical /non mathematic business partner -- Does anyone have insight on how to inherently simply define and explain the difference between an association measure and a p-value when it comes to the association tool when comparing variable association?
Screens for context (+adding workflow)
Using Pearson correlation tool i select all variables
- I look at correlation for the variable i was interested in:
When using the association analysis tool I select the following inputs:
- I see association measure same as pearson correlation tool, but now see p-value in analysis
- I assume the *** next to the p values indicate my most significant variables to consider based off my target variable? And I think i can also include these all have a negative linear correlation (as one goes up the other goes down)?
