In this tutorial, you will learn about what is moment coefficient of kurtosis and how to calculate moment coefficient of kurtosis in R.

## Moment Coefficient of Kurtosis

The literal meaning of kurtosis is **peakedness** or **flatness** of the data. The kurtosis measures how peaked or how flat the histogram is relative to the bell-shaped histogram. The bell-shaped histogram is based on normal (Gaussian) distribution.

- The value of kurtosis for a normal (Gaussian) distribution is 3.
- If the histogram has too many observations in the tails compared to normal histogram, then the kurtosis is larger than 3.
- If the histogram has short tails and most of the observations are tightly clustered around the mean, then the kurtosis is less than 3.

The moment coefficient of kurtosis (also known as Pearson's moment coefficient of kurtosis) is denoted by $\beta_2$ and is defined as

`$$\beta_2=\dfrac{m_4}{m_2^2}$$`

The moment coefficient of kurtosis $\gamma_2$ is defined as

`$$\gamma_2=\beta_2-3$$`

where

`$n$`

total number of observations`$\overline{x}$`

sample mean`$m_2 =\dfrac{1}{n}\sum_{i=1}^n (x_i-\overline{x})^2$`

is second sample central moment`$m_4 =\dfrac{1}{n}\sum_{i=1}^n (x_i-\overline{x})^4$`

is fourth sample central moment

To know more about moments, check the tutorial on how to compute raw and central moments using R with examples.

Note that, the normal curve (Gaussian curve) is a bell-shaped the value of $\beta_2$ is 3.

### Leptokurtic distribution

The distribution is said to be *leptokurtic*, if it has a higher peak than the normal curve.

### Platykurtic Distribution

The distribution is said to be *platykurtic*, if it has a lower peak than the normal curve.

### Mesokurtic Distribution

The distribution is said to be *mesokurtic*, if it is neither peaked nor flat.

## Interpretation of coefficient of skewness

- If $\beta_2 > 3$ or $\gamma_2 > 0$, then the distribution is
**leptokurtic**. - If $\beta_2 = 3$ or $\gamma_2 = 0$, then the distribution is
**mesokurtic**. - If $\beta_2 < 3$ or $\gamma_2 < 0$ then the distribution is
**platykurtic**.

## Moment coefficient of kurtosis using R

To calculate the moment coefficient of kurtosis, we need to install the package `moments`

. The function `kurtosis()`

gives the `$\beta_2=\dfrac{m_4}{m_2^2}$`

coefficient for kurtosis. This function compute the estimators of Pearson's measure of kurtosis based on moments.

The syntax of the `kurtosis()`

function is

`kurtosis(x,na,rm=FALSE)`

where,

**x :**a numeric vector, matrix or data frame**na.rm :**logical value (default`FALSE`

). Should missing values be removed?

## Numerical Problem moment coefficient of kurtosis using R

### Example 1 : Moment Coefficient of Kurtosis using R

The following data are the heights, correct to the nearest centimeters, for a group of children:

126, 129, 129, 132, 132, 133, 133, 135, 136, 137,

137, 138, 141, 143, 144, 146, 147, 152, 154, 161.

Find Moment coefficient of kurtosis and interprete the result.

```
# create a data vector
x <- c(126, 129, 129, 132, 132, 133, 133, 135, 136, 137,
137, 138, 141, 143, 144, 146, 147, 152, 154, 161)
```

```
# load the package
library(moments)
## Compute kurtosis
kurtosis(x)
```

`[1] 2.831218`

The `$\beta_2$`

coefficient of kurtosis is 2.8312182. As $\beta_2 < 3$, the data about height (in cm) is $\text{platykurtic}$.

### Example 2: Moment Coefficient of Kurtosis using R

Blood sugar level (in mg/dl) of a sample of 20 patients admitted to the hospitals are as follows:

75, 80, 72, 78, 82, 85, 73, 75, 97, 87,

84, 76, 73, 79, 99, 86, 83, 76, 78, 73.

Find moment coefficient of kurtosis and interprete the result.

```
BS <-c(75, 80, 72, 78, 82, 85, 73, 75, 97, 87,
84, 76, 73, 79, 99, 86, 83, 76, 78, 73)
```

```
# load the package
library(moments)
## Compute skewness
kurtosis(BS)
```

`[1] 3.571845`

The `$\beta_2$`

coefficient of kurtosis is 3.5718448. As $\beta_2 > 3$, the data about Blood Sugar level is $\text{leptokurtic}$.

## Endnote

In this tutorial you learned about what is moment coefficient of kurtosis and how to calculate moment coefficient of kurtosis using R.

To learn more about descriptive statistics using R, please refer to the following tutorials:

- Statistical functions in R
- Bowley's Coefficient of Skewness using R
- Karl Pearson's Coefficient of Skewness using R
- Kelly's Coefficient of Skewness using R
- Moments Coefficient of Skewness using R
- Descriptive Statistics Using R

Hopefully you enjoyed learning this tutorial on how to compute moment coefficient of kurtosis using R.