Try a correlation using the inter quartile ranges.
-This would remove data outliers correct? Are you sure this is the best way to remove the outliers? I know matlab has this cookie-cutter “smooth data” feature in its curve fitting toolkit that smooths the data nicely. Or I may just remove the outer quartiles as you suggest.
Then try it for different bands of starting LENGTH.
-Do you mean to split the girth data based on starting length? Or are you just saying to do the same things for starting length as you are proposing for girth?
My first guess is that the ultimate correlation while negative as you suggest is far less negative than that trend line. Also try it for guys who stuck with it the same amount of time (a bivariate correlation of gains against length of career and starting girth)
-Cool. I will look into this.
Maybe guys with big girth are less motivated because they have “big dicks” to start. So if you if you look at guys who gave it the same amount of time and effort as PE’ers that the gains in length will be smaller for thicker girth but much less than that line indicates, and your statement is a partially a psychological fact rather than a purely anatomical one. The true anatomical one would be more interesting.
-I agree. I think adding the length of PE career into the analysis should help.
-Still bitter the y2k bug was a dud.
-My dear boy, do you ask a fish how it swims? (No.) Or a bird how it flies? (No.) Of course not. They do it because they were born to do it...