If a random variable X has a Normal distribution with mean M and variance S2, then Z = (X - M)/S
No. The variance of any distribution is the sum of the squares of the deviation from the mean. Since the square of the deviation is essentially the square of the absolute value of the deviation, that means the variance is always positive, be the distribution normal, poisson, or other.
The independent variable explains .32*100 percent of the variance in the dependent variable.This is 9%.The explainable variance is always the square of the correlation (r).
It could be a random variable with a discrete uniform distribution over the range 1 to 6.
It means that the variance remains the same across the range of values of the variable.
To find the Z score from the random variable you need the mean and variance of the rv.To find the Z score from the random variable you need the mean and variance of the rv.To find the Z score from the random variable you need the mean and variance of the rv.To find the Z score from the random variable you need the mean and variance of the rv.
23.18
Z is a variable with mean 0 and variance 1.Z is a variable with mean 0 and variance 1.Z is a variable with mean 0 and variance 1.Z is a variable with mean 0 and variance 1.
Variance" is a mesaure of the dispersion of the probability distribution of a random variable. Consider two random variables with the same mean (same aver-age value). If one of them has a distribution with greater variance, then, roughly speaking, the probability that the variable will take on a value far from the mean is greater.
Variable overhead cost variance is that variance which is in variable overheads costs between the standard cost and the actual variable cost WHILE fixed overheads cost variance is variance between standard fixed overhead cost and actual fixed overhead cost.
If a random variable X has a Normal distribution with mean M and variance S2, then Z = (X - M)/S
It is a discrete random variable.
No. The variance of any distribution is the sum of the squares of the deviation from the mean. Since the square of the deviation is essentially the square of the absolute value of the deviation, that means the variance is always positive, be the distribution normal, poisson, or other.
When it is random it is variable.
This is supposed to be Y > u
efficiency variance, spending variance, production volume variance, variable and fixed components
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.