0
I believe you want the equation to calculate the standard deviation of a sample. The equation is: s = square root[ sum from i =1 to n of (xi- xbar)/(n-1)] where xbar is the average of values of the sample and n = size of sample.
Wiki User
∙ 15y ago
Add your answer:
Earn +20 pts
Sample standard deviation?
Standard deviation in statistics refers to how much deviation there is from the average or mean value. Sample deviation refers to the data that was collected from a smaller pool than the population.
What does the sample standard deviation best estimate?
The standard deviation of the population. the standard deviation of the population.
How do you calculate the parameter to a 99.9 confidence interval using mean and standard deviation?
Did you mean, "How do you calculate the 99.9 % confidence interval to a parameter using the mean and the standard deviation?" ? The parameter is the population mean μ. Let xbar and s denote the sample mean and the sample standard deviation. The formula for a 99.9% confidence limit for μ is xbar - 3.08 s / √n and xbar + 3.08 s / √n where xbar is the sample mean, n the sample size and s the sample standard deviation. 3.08 comes from a Normal probability table.
Can a standard deviation of a sample be equal to a standard deviation of a population?
Yes
How do you find the sample deviation?
You're an idiot. It's standard deviation. Google that for your answer.
What is the sample standard deviation of 27.5?
A single observation cannot have a sample standard deviation.
Sample standard deviation?
Standard deviation in statistics refers to how much deviation there is from the average or mean value. Sample deviation refers to the data that was collected from a smaller pool than the population.
What is the standard error if the population standard deviation is 100 and the sample size is 25?
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.
What does the sample standard deviation best estimate?
The standard deviation of the population. the standard deviation of the population.
Can a standard deviation of a sample be equal to a standard deviation of a population?
Yes
How do i find sample standard deviation from population standard deviation?
If the population standard deviation is sigma, then the estimate for the sample standard error for a sample of size n, is s = sigma*sqrt[n/(n-1)]
How do you calculate the parameter to a 99.9 confidence interval using mean and standard deviation?
Did you mean, "How do you calculate the 99.9 % confidence interval to a parameter using the mean and the standard deviation?" ? The parameter is the population mean μ. Let xbar and s denote the sample mean and the sample standard deviation. The formula for a 99.9% confidence limit for μ is xbar - 3.08 s / √n and xbar + 3.08 s / √n where xbar is the sample mean, n the sample size and s the sample standard deviation. 3.08 comes from a Normal probability table.
How does one calculate the standard error of the sample mean?
Standard error of the sample mean is calculated dividing the the sample estimate of population standard deviation ("sample standard deviation") by the square root of sample size.
What happens if you estimate population standard deviation with the sample standard deviation?
Not a lot. After all, the sample sd is an estimate for the population sd.
What is the statistic that is used to estimate a population standard deviation?
the sample standard deviation
How do you find the sample deviation?
You're an idiot. It's standard deviation. Google that for your answer.
Why does the formula for standard deviation have a square root in it?
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.
