Kurtosis
Kurtosis[list]
gives the coefficient of kurtosis for the elements in list.
Kurtosis[dist]
gives the coefficient of kurtosis for the distribution dist.
Details

- Kurtosis measures the concentration of data around the peak and in the tails versus the concentration in the flanks.
- A normal distribution has kurtosis
equal to 3. In comparing shapes with normal we have:
-
more flat than normal, platykurtic like normal, mesokurtic more peaked than normal, leptokurtic - Kurtosis handles both numerical and symbolic data.
- Kurtosis[{{x1,y1,…},{x2,y2,…},…}] gives {Kurtosis[{x1,x2,…}],Kurtosis[{y1,y2,…}],…}.
- Kurtosis[…] is equivalent to CentralMoment[…,4]/CentralMoment[…,2]2.
Examples
open allclose allScope (14)
Data (10)
Exact input yields exact output:
Approximate input yields approximate output:
Kurtosis for a matrix gives column-wise kurtosis:
SparseArray data can be used just like dense arrays:
Find the kurtosis of WeightedData:
Find the kurtosis of EventData:
Find the kurtosis of TemporalData:
Find the kurtosis of TimeSeries:
Distributions and Processes (4)
Find the kurtosis for univariate distributions:
Kurtosis for derived distributions:
Applications (6)
Normal distributions have Kurtosis value 3:
Leptokurtic distributions have kurtosis greater than 3:
Platykurtic distributions have kurtosis less than 3:
The limiting distribution for BinomialDistribution as is normal:
The limiting value of the kurtosis is 3:
By the central limit theorem, kurtosis of normalized sums of random variables will converge to 3:
Define a Pearson distribution with zero mean and unit variance, parameterized by skewness and kurtosis:
Obtain parameter inequalities for Pearson types 1, 4, and 6:
The region plot for Pearson types depending on the values of skewness and kurtosis:
Generate a random sample from a ParetoDistribution:
Determine the type of PearsonDistribution with moments matching the sample moments:
This time series contains the number of steps taken daily by a person during a period of five months:
Analyze the kurtosis as an indication of consistency of daily steps taken:
The histogram of the frequency of daily counts shows that distribution is mesokurtic:
Find the skewness for the heights of the children in a class:
Kurtosis larger than 3 would indicate a distribution highly concentrated around the mean:
Properties & Relations (5)
Kurtosis for data can be computed from CentralMoment:
Kurtosis for a distribution can be computed from CentralMoment:
Kurtosis is bounded from below by 1, as :
Normal distributions have Kurtosis value 3:
Approximately normal distributions have Kurtosis values near 3:
Possible Issues (1)
Kurtosis coefficient is sometimes confused with excess kurtosis coefficient:
The excess kurtosis vanishes for NormalDistribution:
Excess kurtosis is defined as Cumulant[dist,4]/Cumulant[dist,2]^2:
Neat Examples (1)
The distribution of Kurtosis estimates for 20, 100, and 300 samples:
Text
Wolfram Research (2007), Kurtosis, Wolfram Language function, https://reference.wolfram.com/language/ref/Kurtosis.html.
CMS
Wolfram Language. 2007. "Kurtosis." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/Kurtosis.html.
APA
Wolfram Language. (2007). Kurtosis. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/Kurtosis.html