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From |
"Michael S. Hanson" <mshanson@mac.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: - uniform() - random variable not uniformly distributed overinterval |

Date |
Wed, 9 Mar 2005 21:05:28 -0500 |

On Mar 9, 2005, at 8:19 PM, Deborah Garvey wrote:

I confess I don't fully understand what you are doing, but in light of your message I have a comment below....I am generating a random number with the goal of (randomly) assigning children who report multiple races to a single race category for purposes of analysis. I'd like to evenly distribute children across single race categories.

when I use uniform() to generate a random number, I don't get an even split:It does equal 1/2 -- within the sampling error inherent in drawing random numbers from a given population distribution. If you always got *exactly* 1/2 of "random" sample from a uniform distribution to be on either side of the mean/median, you should be very, very skeptical of your random number generator!

. gen r = uniform() if k_race == 801

(363374 missing values generated)

. su r

Variable | Obs Mean Std. Dev. Min Max

-------------+--------------------------------------------------------

r | 2309 .4995691 .2914078 .0007709 .9993796

. gen byte k_race2 = .

(365683 missing values generated)

. replace k_race2 = 100 if r <= 0.5

(1151 real changes made)

. replace k_race2 = 200 if r > 0.5 & r <= 1

k_race2 was byte now int

(1158 real changes made)

I am puzzled. I would expect median = mean = 0.5 for a uniform number

defined over [0,1).

Thus, my quick comment: don't expect your "randomly" re-assigned race categories to reproduce the distribution of the remaining race categories in your larger sample -- that's not how random variables (should) work. Even worse would be to find some way to force the "randomly" re-assigned individuals to follow the sample distribution rather than let them be chosen randomly given your population assumptions -- assuming you truly desire random assignment. As I said above, I don't understand your application well enough to advise you here. HTH.

-- Mike

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**References**:**st: - uniform() - random variable not uniformly distributed overinterval***From:*"Deborah Garvey" <DGarvey@scu.edu>

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