# Define Quartile Deviation

Learn about quartile deviation, a statistic that measures variability in a dataset. Discover how to calculate it and its significance in statistics. Explore examples and case studies.

## Understanding Quartile Deviation

Quartile deviation is a measure of variability in a dataset that provides insight into the spread of the data. It is calculated by finding the difference between the third quartile (Q3) and the first quartile (Q1) of the data set. This statistic is useful in determining the dispersion of values around the median.

## Calculating Quartile Deviation

To calculate quartile deviation, first find the values of Q1 and Q3. Then, subtract Q1 from Q3 to get the quartile deviation. The formula is: QD = (Q3 – Q1) / 2.

## Example

Consider the following data set: 10, 15, 20, 25, 30, 35.

• Q1 = 15
• Q3 = 30
• Quartile Deviation = (30 – 15) / 2 = 7.5

## Significance of Quartile Deviation

Quartile deviation is important in statistics as it provides a measure of variability that is not affected by extreme values or outliers in the data set. It gives a more robust estimate of the spread of data compared to the range.

## Case Study

In a study of student test scores, quartile deviation was used to measure the variability in scores across different subjects. The researchers found that subjects with a higher quartile deviation had more varied scores among students.

## Statistics

According to recent research, quartile deviation is becoming increasingly popular in financial analysis to measure risk and volatility in investment portfolios. Investors use quartile deviation to make informed decisions about diversification and risk management.