Nick,
As always, you enlighten and stimulate! I like the concept of Q-Q Plots. I
typically use them in conjunction with propensity scoring to demonstrate
(visually)that the matches achieved balance on baseline characteristics.
That said, there are some audiences that prefer to see something that looks
and feels like a histrogram or density curve to see how the distributions
overlap (or don't). In this situation, I usually offer up a couple different
methods of visually describing the data.
Thank you for your insights.
Ariel
Date: Sun, 22 Nov 2009 15:28:30 -0000
From: "Nick Cox" <[email protected]>
Subject: RE: st: overlaying two histograms (or distribution curves)
For comparison of precisely two distributions -- especially when there
is not a prior prejudice or hypothesis that we are expecting
approximations to some named, equation-specified distribution -- I
regard quantile-quantile plots as near optimal. They allow you to focus
on both similarity and differences and to think directly in terms of
what is being measured.
Why is this graph form which is
(a) information-rich
(b) free of arbitrary assumptions (bin or kernel width, etc.)
(c) easy to explain
(d) easy to compute
(e) well documented
not enormously better known?
See -qqplot-.
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