Thanks all! I did not want to cause any trouble and, God forbid,
offense. I thought, I asked a simple question to improve my
understanding and R-skills.
It seems that there ain't single gospel truth about QQs. :-)
Thanks, again!
Best,
PM
Juliet Hannah wrote:
I think it's helpful to show the sampling variability in a QQ plot
under repeated
sampling. An example is given
in Venables, Ripley pg 86. The variance is higher at the tails. Even when the
distributions are the same, the QQ plot does not have to resemble a straight
line because of sampling. I don't think you can think of any one of these as the
"correct" plot.
Also, if the two
data sets have an equal number of points, the empirical qq plot is
simply a plot of
one sorted data set against the other. (Kundu, Statistical Computing, pg 42).
On Sun, Sep 27, 2009 at 9:06 AM, Duncan Murdoch <murd...@stats.uwo.ca> wrote:
Eric Thompson wrote:
The supposed example of a Q-Q plot is most certainly not how to make a
Q-Q plot. I don't even know where to start....
First off, the two "Q:s in the title of the plot stand for "quantile",
not "random". The "answer" supplied simply plots two sorted samples of
a distribution against each other. While this may resemble the general
shape of a QQ plot, that is where the similarities end.
The empirical quantiles of a sample are simply the sorted values. �You can
plot empirical quantiles of one sample versus some version of quantiles from
a distribution (what qqnorm does) or versus empirical quantiles of another
sample (what Sunil did). �The randomness in his demonstration did two
things: it generated some data, and it showed the variability of the plot
under repeated sampling.
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