I have two variables:
1) Percentage of population of different geographic regions which has bought a certain category of product (say hair sprays) in the last MONTH
2) Percentage of population which has bought a specific brand of hair spray in the last YEAR.
I am trying to in which region the brand is doing better compared to the general use of hair spray, and in which it is doing worse.
Since the first variable is for a month and the second for a whole year, i can't just compare them like that. So I thought i'll transform them into z-scores, and take the difference between the two. Question: Is that a valid approach?
(Side question: the distribution of the second variable has quite a strong positive skew, while the first is fairly normal. Is that a problem?)
Thanks in advance for any advice!
EDITS in response to questions:
- For variable 2 I only have data for that one particular brand, so I can't aggregate across brands. While variable 1 is only for the whole product category encompassing all brands - can't dissaggregate that either.
- Sample size is over 100 geographical regions
- I am attaching a scatter plot of the two variables. One of which shows variable 2 on a log scale.
- Just to clarify, I am not so much interested in the overall relationship between the two variables, but more in arriving at a metric that will tell me for any specific entity (region) how well it is doing on variable 2 (sales of specific brand) compared to variable 1 (sales of general product category). Now, of course, I can see that maybe the regression/scatterplot approaches suggested might be an intermediate step to achieve that goal. But how do I progress after that?
Thanks so much for the suggestions so far though!