Hi Scott
When I create artificial data, I try to "copy" some real. So I mesure
the real data with as much parameters than I can (mean, var, cov, but
also percent of NA, outlier), then I generate the artificial one. It is
also possible to generate several sets that I finaly mixe (lika one set
for men, one set for women. Then I remove the variable "gender", I merge
the two set and I shuffle the resulting set.
Christophe
Hi everyone-
I'm currently teaching a graduate course in statistics for linguistics
using R. I have used up most of the 'authentic' data I have been able
to collect for homework and demonstrations. I can think of plenty more
possible data sets, but I am finding the creation of them challenging,
and my creations are often somewhat unlealistic (generally, too
'neat' and obvious).
So, I was wondering if anyone had any tips on creating 'realistic'
data sets, or links/books that describe it.
For a simple example, let's say I want to create a dataset with
students from different countries and academic departments who took an
English test. I want to make some differences (significant and not)
and possibly even interactions among the scores by country and
department. I have been doing this through various iterations of
sample() and rnorm(), and jitter() to get some randomness, but things
are still coming out pretty neatly. Is this the right (or a good)
method? Advice?
Thanks in advance-
SFK
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