>>>Say I have a set of data that is the price of a home.
>>>
>>>100,000
>>>125,000
>>>92,000
>>>175,000
>>>135,000
>>>10,000
>>>500,000
>>>
>>>Well, I know that the 10,000 fiqure is really just for a lot. So when figuring trends, I need to throw it out. I also know that the 500,000 fiqure is also way off, so it too needs to be thrown out.
>>>
>>>If each of these numbers were in a field in a dbf(one per record) How could I programmatically throw out numbers that were way off? I have tried diffrent forms of average and standard deviation, but nothing seems quite right. I need some high school algebra or statistics lessons I think.
>>
>>If you're seeking trends, I'd think about using the median. The housing industry actually often uses this (the middle value of the set from a specific time period). How about that?
>
>I will give it shot. I just need to figure out what is acceptable deviation.

Caught me just before logoff...there isn't any deviation with median, you just sort all your values and take the middle one (if odd number) or (middle 2 summed)/2 if even. Unless you are certain some values are totally bogus, of course...the median factors out outliers automatically.
Say you have one house that was a Swiss castle sold for 45 million $, and a shack in desert that sells for 10$. So what, they have no weight as long as at least 3 values exist. That's why median is used for home prices and employee salaries more often than mean, which picks up the effects of outliers...