**Measure of Kurtosis**

**Measure of Kurtosis**

kurtosis is a measure of peakedness of a distribution relative to the normal distribution. A distribution having a relatively high peak is called leptokurtic. A distribution which is flat topped is called platykurtic. The normal distribution which is neither very peaked nor very flat-topped is also called mesokurtic. The histogram is an effective graphical technique for showing both the skewness and kurtosis of data set.[Latexpage]

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.

Moment ratio and Percentile Coefficient of kurtosis are used to measure the kurtosis

Moment Coefficient of Kurtosis= $b_2 = \frac{m_4}{S^2} = \frac{m_4}{m^{2}_{2}}$

Percentile Coefficient of Kurtosis = $k=\frac{Q.D}{P_{90}-P_{10}}$

where Q.D = $\frac{1}{2}(Q_3 – Q_1)$ is the semi-interquartile range. For normal distribution this has the value 0.263.

A normal random variable has a kurtosis of 3 irrespective of its mean or standard deviation. If a random variable’s kurtosis is greater than 3, it is said to be **Leptokurtic**. If its kurtosis is less than 3, it is said to be** Platykurtic**.

#### Incoming search terms:

- distribution that is relatively peaked with a low degree of dispersion has a coefficient of kurtosis
- 0 263 is leptokurticor platykurtic
- percentiles measures of kurtosis
- Percentileccoefficient of kurtosis of normal
- Percentile Coefficient of kurtosis of normal distribution
- Percentile Coefficient of kurtosis of normal
- percentile coefficient of kurtosis
- peakedness graphs
- co-efficient of kurtosis based upon percentiles
- SKEWNESS MEASURES PEAK TO VALLEY RATIO

Thanks for update