a=(0-100)
when x=0, a should be 0 when x=100, a should be 100
the data needs to bell curve towards the 100 mark, so that once x passes 100 a will remain at 100 and not go over.
Explanation and application follows:
We have a number of rows of data that are counted as good, bad or questionable. If a row is bad we count it as full value (1.0) against the total. so 100 rows with o1 bad = 99% success if a row is questionable, we count it as a percentage of 1 against (maybe .75) so 100 rows with 1 questionable results in 99.25% success
I would like to build in a factor to apply to that value (bad affect or questionable affect) that would reduce it to zero affect (in either case) if there is only 1 row of data. so.. some thing like: 1 row with 1 bad or questionable = 100% success (no matter the questionable affect) 2 rows with one bad would yield nearly 100% success 10 rows with one bad might yield a 99% success rate 50 rows with 1 bad would yield 99.5% 100 rows with one bad would yield 99%
similar affect to questionable results
This factor that I am attempting to derive would be applied to the affect variable for each of bad and questionable affects.
The factor also will have no implication on the affect once it reaches a certain value, in the above sample 100. it will always start at 0.
Thanks for any assistance. -Scott