Standard deviation is the square root of the variance. Since you stated the variance is 4, the standard deviation is 2.
A sample of size 100.
Z = (x-mu)/sigma. So, for your example, any x value can be transformed to Z-score by the formula Z = (x-100)/20.
The probability of scoring an exact value for a continuous variable is zero for any value. The probability of scoring 115 (or more) is 15.87%
To convert 9% to a decimal divide by 100: 9% ÷ 100 = 0.09
Standard deviation is the square root of the variance. Since you stated the variance is 4, the standard deviation is 2.
The SD is 2.
6.3
If you've computed the variance of a statistic that is 57312725112532100 then the standard deviation is the positive square root of itGoogle calc--->sqrt(57 312 725 112 532 100) = 239 400 763
The variance is 247.9. The StDev. is the square root of the variance: 15.75. See http://www.mathsisfun.com/standard-deviation.htmlfor an easy-to-understand discussion of StDev and Var.
Standard deviation is 0.
It can be.
From a statistical sense, variance is basically a measure of how spread out the data is from the mean (center) observation. For example, if a company has a mean profit of $100 from all their sales, then we might compute the variance which say for sake of argument is $16. Then what we would do is use the variance number and take the square root to find what is called the standard deviation. In this case the standard deviation would be $4. (The square root of $16). Then we could say that we are 68% sure that the true profit is within the range of the mean plus or minus the standard deviation. In our example, we would have the range as being 100-4=96 to 100+4=104. So we can say that we are 68% sure that the true profit is within ($96, $104) This could be extended further for more confidence... This is just an example of how we use variance. Just think of it as spread. As for negative profit variance..I think that would simply mean that we are looking at loses. It would be similar to above example, example that our average would be -$100 instead.
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
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.
square (25/36) = 5/6 = .833
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