Skewness is a measure of symmetry, or more precisely, the lack of symmetry. A distribution, or data set, is symmetric if it looks the same to the left and right of the center point.
Kurtosis is a measure of whether the data are peaked or flat relative to a normal distribution.
See related link. By doing a search on the internet, you can find more examples.
Chat with our AI personalities
There are many, many formulae:for different probability distribution functions,for cumulative distribution functions,for moment generating functions,for means, variances, skewness, kurtosis and higher moments.There are many, many formulae:for different probability distribution functions,for cumulative distribution functions,for moment generating functions,for means, variances, skewness, kurtosis and higher moments.There are many, many formulae:for different probability distribution functions,for cumulative distribution functions,for moment generating functions,for means, variances, skewness, kurtosis and higher moments.There are many, many formulae:for different probability distribution functions,for cumulative distribution functions,for moment generating functions,for means, variances, skewness, kurtosis and higher moments.
if coefficient of skewness is zero then distribution is symmetric or zero skewed.
distinguish between dispersion and skewness
Skewness is measured as the third standardised moment of the random variable. Skewness is the expected value of {[X - E(X)]/sd(X)}3 where sd(X) = sqrt(Variance of X)
The Greek word "kurtosis", when translated to English, means the probability theory of any measure of the "peakedness" of a real valued random variable.