kurtosis

As a letter s

is the letter s with a.

As a noun kurtosis is

(statistics) a measure of "peakedness" of a probability distribution, defined as the fourth cumulant divided by the square of the variance of the probability distribution.

is likely misspelled.

has no English definition.

As a noun kurtosis

is a measure of "peakedness" of a probability distribution, defined as the fourth cumulant divided by the square of the variance of the probability distribution.

As nouns the difference between kurtosis and kurtoses

is that kurtosis is a measure of "peakedness" of a probability distribution, defined as the fourth cumulant divided by the square of the variance of the probability distribution while kurtoses is plural of kurtosis.

In statistics terms the difference between skewness and kurtosis

is that skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable; is the third standardized moment, defined as $\backslash scriptstyle\backslash gamma\_1\; =\; \backslash frac\{\backslash mu\_3\}\{\backslash sigma^3\},\; \backslash !$ where $\backslash mu\_3$ is the third moment about the mean and $\backslash sigma$ is the standard deviation while kurtosis is a measure of "peakedness" of a probability distribution, defined as the fourth cumulant divided by the square of the variance of the probability distribution.

As nouns the difference between skewness and kurtosis

is that skewness is the property of being skew while kurtosis is a measure of "peakedness" of a probability distribution, defined as the fourth cumulant divided by the square of the variance of the probability distribution.

In statistics|lang=en terms the difference between kurtosis and peakedness

is that kurtosis is (statistics) a measure of "peakedness" of a probability distribution, defined as the fourth cumulant divided by the square of the variance of the probability distribution while peakedness is (statistics) kurtosis.

As nouns the difference between kurtosis and peakedness

is that kurtosis is (statistics) a measure of "peakedness" of a probability distribution, defined as the fourth cumulant divided by the square of the variance of the probability distribution while peakedness is (chiefly|in combination) the condition of having a (specified form of) peak.