Assertion: sample's distribution | /r/HasProperty | symmetric
Facets:
value: about its mean
type: OTHER
value: around zero
type: DEGREE
value: necessarily
type: DEGREE
Source sentences:
text: The distribution is still symmetric, but it is not uniform.
text: For most distributions of v i {\displaystyle v_{i}} (but not Mammen's), this method assumes that the 'true' residual distribution is symmetric and can offer advantages over simple residual sampling for smaller sample sizes.
text: If the bootstrap distribution of an estimator is symmetric, then percentile confidence-interval are often used; such intervals are appropriate especially for median-unbiased estimators of minimum risk (with respect to an absolute loss function).
text: The distribution shown in Figure 4 is symmetric; if you folded it in the middle, the two sides would match perfectly.
text: The normal distribution is symmetric about its mean, and is non-zero over the entire real line.
text: This distribution is symmetric around zero, unbounded at z = 0 {\displaystyle z=0} characteristic function ϕ Z ( t ) = ( 1 + t 2 ) − 1 / 2 {\displaystyle \phi _{Z}(t)=(1+t^{2})^{-1/2}}
text: This distribution is symmetric around zero, unbounded at characteristic function
text: For example, ask: would I expect this distribution to be symmetric, and am I
text: A normal distribution is symmetric above the mean which means that the data near the mean is more likely to occur as opposed to the data that is far from the mean.
text: An interesting observation can be made in the case where the distribution is symmetric, and the population median equals the population mean.
text: As mentioned earlier, a unimodal distribution with zero value of skewness does not imply that this distribution is symmetric necessarily.
text: If the distribution is symmetric, then the mean is equal to the median, and the distribution has zero skewness.
text: If the distribution is both symmetric and unimodal, then the mean = median = mode.