33.79
I used the formula SD = sq rt {Sigma[(x - mean)^2]/n} where sigma means the sum of; ^2 means squared.
Firstly, find the mean of the data = 50.
Subtract each term separately from the mean and square it.
Add these 10 terms and it comes to 11424
Divide by the number of terms, 10 = 1142.4
Then find the sq rt = 33.79.
The formula for standard deviation has both a square (which is a power of 2) and a square-root (a power of 1/2). Both must be there to balance each other, to keep the standard deviation value's magnitude similar to (having the same units as) the sample numbers from which it's calculated. If either is removed from the formula, the resulting standard deviation value will have different units, reducing its usefulness as a meaningful statistic.
No, a standard deviation or variance does not have a negative sign. The reason for this is that the deviations from the mean are squared in the formula. Deviations are squared to get rid of signs. In Absolute mean deviation, sum of the deviations is taken ignoring the signs, but there is no justification for doing so. (deviations are not squared here)
There must be a formula, but in the mean time there is a handy site that does it for you. [See related link below for the converter]
The mean must be 0 and the standard deviation must be 1. Use the formula: z = (x - mu)/sigma
Q3-q1
Standard deviation is a way to describe how the data is distributed around the Arithmatic Mean. It is not a simple formula to calculate, as shown in the links.
Arithmatic Mean
%E= d/L
b-a/6
Standard deviation is a way to describe how the data is distributed around the Arithmatic Mean. It is not a simple formula to calculate, as shown in the links.
3 (mean − median) / standard deviation.
The formula for standard deviation has both a square (which is a power of 2) and a square-root (a power of 1/2). Both must be there to balance each other, to keep the standard deviation value's magnitude similar to (having the same units as) the sample numbers from which it's calculated. If either is removed from the formula, the resulting standard deviation value will have different units, reducing its usefulness as a meaningful statistic.
Standard deviation (SD) is neither biased nor unbiased. Estimates for SD can be biased but that depends on the formula used to calculate the estimate.
100 x (standard deviation/mean)
Coefficient of deviation (CV) is a term used in statistics. It is defined as the ratio of the standard deviation (sigma) to the mean (mu). The formula for CV is CV=sigma/mu.
Formula for standard error (SEM) is standard deviation divided by the square root of the sample size, or s/sqrt(n). SEM = 100/sqrt25 = 100/5 = 20.
It is a bit complicated; you can find the details here: http://en.wikipedia.org/wiki/Standard_deviation