
Edward A. answered 02/28/20
Math Tutor, Retired Computer Scientist and Technical Communicator
Luc, it turns out, you CAN adjust the standard deviation accurately, using a VERY useful algebraic identity.
As Dan said, we don’t have all the xi or the xi - mean, but here’s the nice identity:
sum( (xi - mean )2) = sum(xi2) - (1/n)( sum(xi) )2
in other words,
(sum of the squares) minus (1/n)*(square of the sum)
I’ll show you how it helps.
We will start with the original numbers, before the error was detected:
STD = 10
Sum(xi) = 4000
using the new identity,
std = sqrt(sum(xi2) - (1/n)( sum(xi) ))2
From this we can solve for sum(xi2)
std2= sum(xi2) - (1/n)(( sum(xi) )2)
sum(xi2) = std2 + (1/n)( (sum(xi))2 )
100 + 40002/100 = 160100
Now we can change those numbers to make the new standard deviation
The new sum(xi ) is the old sum minus the erroneous scores (30 and 70) plus the correct values (3 and 27) =
4000 -30 -70 + 3 + 27 = 3930
The new sum(xi2) Is the old sum minus the squares of the erroneous scores (900 and 4900) plus the squares of the correct scores (9 and 729) = 160100 -5800 + 738 = 154300+738 = 155038
Now the new standard deviation is
std = sqrt(sum(xi2) - (1/n)( (sum(xi))2)
= sqrt( 155038 - 39302/100)
= sqrt( 155038 - 15444900/100)
= sqrt( 155038 - 154449)
= sqrt( 589 ) = roughly 24.25
I think the most important lesson of this exercise is that identity
sum( (xi - mean )2) = sum(xi2) - (1/n)( sum(xi) )2
Dan D.
02/27/20