Let
X = ( ∣ t − 1 5 3 2 7 8 4 6 ∣ ∣ t − 1 6 3 2 7 7 4 2 ∣ ∣ t − 1 7 3 4 5 2 3 5 ∣ ∣ t − 1 8 4 7 8 3 2 9 ∣ ∣ t − 1 9 8 2 3 8 7 7 ∣ ∣ t − 2 0 0 3 4 2 3 5 ∣ ∣ t − 2 1 4 8 3 9 8 4 ∣ ∣ t − 2 2 5 3 4 5 3 6 ∣ ∣ t − 2 3 5 4 3 7 8 9 ∣ ∣ t − 2 4 2 3 4 5 1 3 ∣ ) 1 0 1
where
t = 1 0 1 5 3 2 7 8 4 6 + 1 6 3 2 7 7 4 2 + 1 7 3 4 5 2 3 5 + 1 8 4 7 8 3 2 9 + 1 9 8 2 3 8 7 7 + 2 0 0 3 4 2 3 5 + 2 1 4 8 3 9 8 4 + 2 2 5 3 4 5 3 6 + 2 3 5 4 3 7 8 9 + 2 4 2 3 4 5 1 3
Out of the given options, choose the most precise upper bound of X , without using a calculator.
This section requires Javascript.
You are seeing this because something didn't load right. We suggest you, (a) try
refreshing the page, (b) enabling javascript if it is disabled on your browser and,
finally, (c)
loading the
non-javascript version of this page
. We're sorry about the hassle.
The upper bound on the standard deviation is 1/2 the range, so 8906667/2. The closest number to this of those given is the smallest, 8906700.
Log in to reply
https://www.thoughtco.com/range-rule-for-standard-deviation-3126231
Actually, I just came across this article. Shouldn't it be 8906667/4 instead?
Log in to reply
No, range/4 is a very rough approximation for the standard deviation of a data set; it is not an upper bound. For example, the actual standard deviation of your data set is 3,050,512 which is roughly range/3 > range/4.
It seems like the thrust of your problem as you have stated it is for folks to recognize that you are talking about the standard deviation, and then to get a bound based on a simple hand calculation. The simplest bound is range/2. In the case where all the data points are at the endpoints of the interval, this is the actual standard deviation. For more clustered data, it is merely an upper bound (and range/4 is a guess for what the actual value would be).
Thanks for posting the problem!
Log in to reply
@G Silb – Yeah but the problem stated to find the upper bound of X, not the upper bound of the standard deviation, so approximating the standard deviation could also be a way to do it, right?
Problem Loading...
Note Loading...
Set Loading...
Using the fact that the geometric mean is always less or equal to the quadratic mean, we get:
X = ( ∣ t − 1 5 3 2 7 8 4 6 ∣ ∣ t − 1 6 3 2 7 7 4 2 ∣ ∣ t − 1 7 3 4 5 2 3 5 ∣ ∣ t − 1 8 4 7 8 3 2 9 ∣ ∣ t − 1 9 8 2 3 8 7 7 ∣ ∣ t − 2 0 0 3 4 2 3 5 ∣ ∣ t − 2 1 4 8 3 9 8 4 ∣ ∣ t − 2 2 5 3 4 5 3 6 ∣ ∣ t − 2 3 5 4 3 7 8 9 ∣ ∣ t − 2 4 2 3 4 5 1 3 ∣ ) 1 0 1 ≤ 1 0 1 ( ( t − 1 5 3 2 7 8 4 6 ) 2 + ( t − 1 6 3 2 7 7 4 2 ) 2 + ( t − 1 7 3 4 5 2 3 5 ) 2 + ( t − 1 8 4 7 8 3 2 9 ) 2 + ( t − 1 9 8 2 3 8 7 7 ) 2 + ( t − 2 0 0 3 4 2 3 5 ) 2 + ( t − 2 1 4 8 3 9 8 4 ) 2 + ( t − 2 2 5 3 4 5 3 6 ) 2 + ( t − 2 3 5 4 3 7 8 9 ) 2 + ( t − 2 4 2 3 4 5 1 3 ) 2 )
But the quadratic mean is just the formula for the standard deviation of this data set with really large numbers and no outliers, which means that the standard deviation can be well approximated using the range:
R x = 4 x m a x − x m i n = 4 2 4 2 3 4 5 1 3 − 1 5 3 2 7 8 4 6 = 4 8 9 0 6 6 6 7
Therefore the best upper bound from the options above is the smallest, 8 9 0 6 7 0 0 .