answersLogoWhite

0

Usually the sum of squared deviations from the mean is divided by n-1, where n is the number of observations in the sample.

User Avatar

Wiki User

11y ago

Still curious? Ask our experts.

Chat with our AI personalities

FranFran
I've made my fair share of mistakes, and if I can help you avoid a few, I'd sure like to try.
Chat with Fran
RossRoss
Every question is just a happy little opportunity.
Chat with Ross
SteveSteve
Knowledge is a journey, you know? We'll get there.
Chat with Steve

Add your answer:

Earn +20 pts
Q: Why do you divide by instead of when calculating the sample variance?
Write your answer...
Submit
Still have questions?
magnify glass
imp
Continue Learning about Statistics

Which three elements are necessary for calculating a confidence interval?

Variance, t-value, sample mean


Show that in simple random sampling the sample variance is an unbiased estimator of population variance?

It is a biased estimator. S.R.S leads to a biased sample variance but i.i.d random sampling leads to a unbiased sample variance.


What is the sample variance and the estimated standard error for a sample of n 4 scores with SS 300?

The sample variance is obtained by dividing SS by the degrees of freedom (n-1). In this case, the sample variance is SS/(n-1) = 300/(4-1) = 300/3 = 100 In order to get the standard error, you can do one of two things: a) divide the variance by n and get the square root of the result: square.root (100/4) = square.root(25) = 5, or b) get the standard deviation and divide it by the square root of n. 10/square.root(4) = 10/2 = 5


Can the variance of a sample be negaTIve?

No.


How do you calculate salary variance?

I believe you are interested in calculating the variance from a set of data related to salaries. Variance = square of the standard deviation, where: s= square root[sum (xi- mean)2/(n-1)] where mean of the set is the sum of all data divided by the number in the sample. X of i is a single data point (single salary). If instead of a sample of data, you have the entire population of size N, substitute N for n-1 in the above equation. You may find more information on the interpretation of variance, by searching wikipedia under variance and standard deviation. I note that an advantage of using the standard deviation rather than variance, is because the standard deviation will be in the same units as the mean.