Or, you normalize with eigenvectors. Just determine the McClaurean
equivalent, factor the Jacobian and viola!

Actually, in a take on Dean's suggestion you could try a weighting
function. Simply assign a numeric value for each classification,
"bucketize" the results by sum of the numeric values for all variables
(you might need to groups ranges), then randomly select from the
resulting buckets to fill your groups.

I'd then take Cameron's suggestion and do a comparison with the break
down of the distribution in the resulting groups and possibly tweak.

On Fri, Jul 25, 2008 at 6:44 PM, Cameron Childress <[EMAIL PROTECTED]> wrote:
> Dean's method is one possibility.  This is actually a very interesting
> question and I'm nojt sure how I'd solve it.  I thought about it a bit
> during my drive home, and here's the approach I would take...  This is
> alot easier if there are only two choices for each statistic
> (male|female - american|foreign - white|nonwhite), but it could also
> work with multiple choices for each.
>
> First, how to measure the success of the program?  Measure the
> percentage of each stat in the group as a whole (I'll call this Big
> Ratio), and then measure the percentages in each of the 25 groups
> (I'll call this Group Ratio) and see how closely they each match.
>
> Okay, next, how to divide them up into groups?  I'd start by seeding
> each group with a random individual.  Then I would take each person
> from the pool of potential students and loop over each group, testing
> to see if adding that person to that group would make the Group Ratio
> for that group closer or farther away from the Big Ratio.  Whichever
> Group Ratio moves the farthest toward the Big Ratio would be the group
> you add that individual to.  Once a group reaches 17 people, close it
> and stop adding people to it.
>
> You'll have to find a way of combining the ratios and determining one
> big number that represents the combination.  I am sure if I paid more
> attention in my statistics class I'd know it had something to do with
> standard deviations, but I didn't pay any attention - so that's up to
> you to figure out.
>
> Once you are done, look at all the Group Ratios and see how close
> their balance measures up to the Big Ratio.
>
> Two suggestions to make this easier on yourself:
> 1) Start by attempting to balance a smaller number of groups than 25.
> 2 or 3 maybe.
> 2) Start with binary choices, then move on to multiple choices after
> you have  amethod that is capible of balancing two choices.
>
> Least that's where I would start.  If you are willing, post your
> solution (in english or in code) once you're done.  I would be
> interested in seeing how you did it.
>
> -Cameron
>
> On Fri, Jul 25, 2008 at 5:25 PM, Tepfer, Seth <[EMAIL PROTECTED]> wrote:
>> I have a challenge laid out before me. I need to divide the incoming Oxford 
>> student class into 25 groups of about 16 or 17 students each. However, they 
>> want the groups to be as balanced as possible, across number, sex, race, and 
>> geographic origin. Now, I can easily see how to balance based on sex or any 
>> single characteristic. But how to balance across all three at the same time? 
>> My head starts spinning when I think about the issues that we won't 
>> necessarily have equal distribution across any of the characteristics.
>>
>> I don't need the code, just the concept. I am having a hard time conceiving 
>> on how to do this if the people were standing in front of me, much less by 
>> code. Any ideas?
>
>
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-- 
Darin Kohles
RIA Developer


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