Noun:

ಸೂಜಿಗಳು,

kurtosis's Usage Examples:

in terms of the higher moments, using the method of moments, as in the skewness (3rd moment) or kurtosis (4th moment), if the higher moments are defined.

In statistics and decision theory, kurtosis risk is the risk that results when a statistical model assumes the normal distribution, but is applied to observations.

Ignoring kurtosis risk will cause any model to understate the risk of variables with high kurtosis.

The kurtosis is here defined to be the standardised fourth moment around the.

"J-shaped", or numerically, using quantitative measures such as skewness and kurtosis.

calculate quantities analogous to standard deviation, skewness and kurtosis, termed the L-scale, L-skewness and L-kurtosis respectively (the L-mean is identical.

central moment is the variance, the third standardized moment is the skewness, and the fourth standardized moment is the kurtosis.

α → 0 excess kurtosis lim β → 0 excess kurtosis lim μ → 0 excess kurtosis lim μ → 1 excess kurtosis ∞ lim α → ∞ excess kurtosis 6 β ,   lim.

analogous to standard deviation, skewness and kurtosis, termed the L-scale, L-skewness and L-kurtosis respectively (the L-mean is identical to the conventional.

Jarque–Bera test is a goodness-of-fit test of whether sample data have the skewness and kurtosis matching a normal distribution.

deviation but different means, the overall distribution will exhibit low kurtosis relative to a single normal distribution – the means of the sub populations.

The variance, skewness, and excess kurtosis can be calculated from these raw moments.

second central moment is the variance, the third standardized moment is the skewness, and the fourth standardized moment is the kurtosis.