Assertion: distribution's variance | /r/HasProperty | equal
Facets:
value: to one
type: OTHER
value: to μ
type: OTHER
value: roughly
type: DEGREE
value: to z.
type: OTHER
value: to 2
type: OTHER
value: to the product of their variances
type: OTHER
value: to σ2
type: OTHER
Source sentences:
text: The factor 1 / 2 {\displaystyle 1/2} in the exponent ensures that the distribution has unit variance (i.e., the variance is equal to one), and therefore also unit standard deviation.
text: The variance of this estimator is equal to the μμ-element of the inverse Fisher information matrix
text: The first of these expressions shows that the variance of s 2 {\displaystyle s^{2}} is equal to 2
text: This entails a minor loss of generality but ensures that the variance of the distribution exists and is equal to σ2.
text: If the variances are equal then S = 1.
text: A much simpler result, stated in a section above, is that the variance of the product of zero-mean independent samples is equal to the product of their variances.
text: If your data comes from a Poisson distribution, then the mean and the variance of your data should be roughly equal.
text: The adjective "standard" indicates the special case in which the mean is equal to zero and the variance is equal to one.
text: From the variance sum law, we know that: which says that the variance of the sampling distribution of the difference between means is equal to the variance of the sampling distribution of the mean for Population 1 plus the variance of the sampling distribution of the mean for Population 2.
text: The standard normal distribution table provides the probability that a normally distributed random variable Z, with mean equal to 0 and variance equal to 1, is less than or equal to z.
text: The variance is also equal to μ.