# Quartile

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## Definition of 'Quartile'

A quartile is a statistical term that refers to the four equal parts into which a data set can be divided. The first quartile is the 25th percentile, the second quartile is the 50th percentile (also known as the median), the third quartile is the 75th percentile, and the fourth quartile is the 100th percentile.

Quartiles are used to divide a data set into four equal parts, and they can be used to compare different data sets. For example, if you have two data sets that represent the income of two different groups of people, you can use quartiles to compare the incomes of the two groups.

The first quartile is the 25th percentile, which means that 25% of the data values are less than or equal to the first quartile. The second quartile is the 50th percentile, which means that 50% of the data values are less than or equal to the second quartile. The third quartile is the 75th percentile, which means that 75% of the data values are less than or equal to the third quartile. The fourth quartile is the 100th percentile, which means that 100% of the data values are less than or equal to the fourth quartile.

Quartiles can be used to identify outliers in a data set. An outlier is a data value that is significantly different from the rest of the data values in the set. Outliers can be identified by comparing the data values to the quartiles. If a data value is more than 1.5 times the interquartile range (IQR) above the third quartile or more than 1.5 times the IQR below the first quartile, it is considered to be an outlier.

Quartiles are a useful tool for summarizing and comparing data sets. They can be used to identify outliers and to compare the distribution of data values in different data sets.

Quartiles are used to divide a data set into four equal parts, and they can be used to compare different data sets. For example, if you have two data sets that represent the income of two different groups of people, you can use quartiles to compare the incomes of the two groups.

The first quartile is the 25th percentile, which means that 25% of the data values are less than or equal to the first quartile. The second quartile is the 50th percentile, which means that 50% of the data values are less than or equal to the second quartile. The third quartile is the 75th percentile, which means that 75% of the data values are less than or equal to the third quartile. The fourth quartile is the 100th percentile, which means that 100% of the data values are less than or equal to the fourth quartile.

Quartiles can be used to identify outliers in a data set. An outlier is a data value that is significantly different from the rest of the data values in the set. Outliers can be identified by comparing the data values to the quartiles. If a data value is more than 1.5 times the interquartile range (IQR) above the third quartile or more than 1.5 times the IQR below the first quartile, it is considered to be an outlier.

Quartiles are a useful tool for summarizing and comparing data sets. They can be used to identify outliers and to compare the distribution of data values in different data sets.

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