# Measures of Absolute Variability

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Measures of absolute variability deal with the dispersion of the data points. This include the following:**Analysis with R**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

- Range –
**range** - Interquartile Range –
**IQR** - Quartile Deviation
- Average Deviation
- Standard Deviation –
**sd**

**Example 1**. The heights (in centimetres) of the 17 BS Stat students in section A23 of Statistical Inference under Dr. Supe were recorded. The data are the following: 151, 160, 162, 155, 154, 154, 153, 168, 169, 153, 158, 166, 152, 157, 150, 169, and 167. Compute the range, interquartile range, quartile deviation, average deviation, and standard deviation.

The range is computed using the function

**range**, while the interquartile range is obtained by

**IQR**. Thus,

The range returned the minimum and maximum values of the data which are 150 and 169, respectively. And for the interquartile range, the output returned the difference between the third and first quartiles. Going back to the range, one might want immediately the difference between the maximum and the minimum values. Well, simply insert this to a function for generality, i.e.

Using this new function, the range would be

For quartile deviation, the formula is given by$$QD=\frac{Q_3-Q_1}{2}$$where:

- $QD$ – quartile deviation
- $Q_3$ – third quartile
- $Q_1$ – first quartile

The quartile deviation of the heights then, follows

Lastly, the mean deviation$$AD=\displaystyle\frac{\displaystyle\sum_{i=1}^{n}\left|X_i-\bar{X}\right|}{n-1}$$where:

- $AD$ – average deviation
- $X_i$ – $i^{th}$ individual observations
- $\bar{X}$ – sample mean
- $n$ – total number of observations

And therefore, the average deviation of the heights is,

**Reference**:

Asaad, Abubakar S. (2011).

*Simplified Biostatistics*. Manila: Rex Book Store, Inc.

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