Kurtosis Formula. Therefore sample kurtosis equals. The expected value for kurtosis with a normal distribution is zero.
We could then classify a distribution from its excess kurtosis. For a sample of n values a method of moments estimator of the population excess kurtosis can be defined as where m 4 is the fourth sample moment about the mean m 2 is the second sample moment about the mean that is the sample variance x i is the i th value and is the sample mean. The expected value for kurtosis with a normal distribution is zero.
Mesokurtic distributions have excess kurtosis of zero.
The greater the value of beta 2 the more peaked or leptokurtic the curve. S 2 sample variance. 1987 1987 1991 1992 1992 1992 1992 1993 1994 1994 1995. Excess kurtosis for normal distribution 3 3 0.
