define and explain A.kurtosis:mesocurtic,platykurtic, and leptokurtic b.contrast the terms c.talk about the variability greatest to least Solution Kurtosis is a measure of whether the data are peaked or flat relative to a normal distribution. That is, data sets with high kurtosis tend to have a distinct peak near the mean, decline rather rapidly, and have heavy tails. Data sets with low kurtosis tend to have a flat top near the mean rather than a sharp peak. A uniform distribution would be the extreme case. Distributions with zero excess kurtosis are called mesokurtic, or mesokurtotic. The most prominent example of a mesokurtic distribution is the normal distribution family, regardless of the values of its parameters. A distribution with positive excess kurtosis is called leptokurtic.In terms of shape, a leptokurtic distribution has a more acute peak around the mean and fatter tails. Examples of leptokurtic distributions include the Cauchy distribution, Student\'s t-distribution, Rayleigh distribution, Laplace distribution, exponential distribution, Poisson distribution and the logistic distribution. Such distributions are sometimes termed super Gaussian. A distribution with negative excess kurtosis is called platykurtic.In terms of shape, a platykurtic distribution has a lower, wider peak around the mean and thinner tails. Examples of platykurtic distributions include the continuous or discrete uniform distributions, and the raised cosine distribution. The most platykurtic distribution of all is the Bernoulli distribution with p = ½ (for example the number of times one obtains \"heads\" when flipping a coin once, a coin toss), for which the excess kurtosis is -2. Such distributions are sometimes termed sub Gaussian..

1. define and explain
A.kurtosis:mesocurtic,platykurtic, and leptokurtic
b.contrast the terms
c.talk about the variability greatest to least
Solution
Kurtosis is a measure of whether the data are peaked or flat relative to a normal
distribution. That is, data sets with high kurtosis tend to have a distinct peak near the mean,
decline rather rapidly, and have heavy tails. Data sets with low kurtosis tend to have a flat top
near the mean rather than a sharp peak. A uniform distribution would be the extreme case.
Distributions with zero excess kurtosis are called mesokurtic, or mesokurtotic. The most
prominent example of a mesokurtic distribution is the normal distribution family, regardless of
the values of its parameters. A distribution with positive excess kurtosis is called leptokurtic.In
terms of shape, a leptokurtic distribution has a more acute peak around the mean and fatter tails.
Examples of leptokurtic distributions include the Cauchy distribution, Student's t-distribution,
Rayleigh distribution, Laplace distribution, exponential distribution, Poisson distribution and the
logistic distribution. Such distributions are sometimes termed super Gaussian. A distribution
with negative excess kurtosis is called platykurtic.In terms of shape, a platykurtic distribution has
a lower, wider peak around the mean and thinner tails. Examples of platykurtic distributions
include the continuous or discrete uniform distributions, and the raised cosine distribution. The
most platykurtic distribution of all is the Bernoulli distribution with p = ½ (for example the
number of times one obtains "heads" when flipping a coin once, a coin toss), for which the
excess kurtosis is -2. Such distributions are sometimes termed sub Gaussian.