# quartile

## Understanding Quartiles in Statistics

Quartiles are statistical measures that divide a dataset into four equal parts, or quarters, based on the values of the data points. They are a form of quantile, which is a broader term that refers to dividing a probability distribution into areas of equal probability. Quartiles are particularly useful for understanding the spread and distribution of a dataset, especially in descriptive statistics and exploratory data analysis.

### Definition of Quartiles

There are three quartiles that divide the data into four sections:

• First Quartile (Q1): Also known as the lower quartile, it is the value that cuts off the first 25% of the data when it is sorted in ascending order. It is the median of the lower half of the dataset.
• Second Quartile (Q2): This is the median of the dataset and cuts the data in half. 50% of the data points are less than or equal to the median, and 50% are greater than or equal to the median.
• Third Quartile (Q3): Known as the upper quartile, it marks the 75th percentile of the data. It is the median of the upper half of the dataset.

The range between the first and third quartiles is known as the interquartile range (IQR). It measures the middle 50% of the data and is a robust measure of the spread or dispersion of the dataset because it is not influenced by outliers or extreme values.

### Calculating Quartiles

To calculate quartiles, the dataset must first be ordered from smallest to largest. The method of calculating quartiles can vary slightly, but a common approach is as follows:

1. Find the median (Q2) of the dataset. If the number of observations is odd, the median is the middle number. If it is even, the median is the average of the two middle numbers.

2. To find Q1, consider the subset of the dataset that falls below the median. The median of this subset is Q1.
3. Similarly, to find Q3, consider the subset of the dataset that is above the median. The median of this upper subset is Q3.

Some statistical software and methodologies might use different methods for calculating quartiles, especially when dealing with even-numbered datasets or when determining how to handle the median value in the Q1 and Q3 calculations.

### Uses of Quartiles

Quartiles are widely used in fields such as finance, economics, medicine, and engineering for various purposes:

• Describing Data: Quartiles provide a simple way to describe the distribution and central tendency of a dataset.
• Identifying Outliers: By using the IQR, statisticians can identify potential outliers, which are data points that fall below Q1 - 1.5*IQR or above Q3 + 1.5*IQR.
• Comparing Distributions: Quartiles can be used to compare the distributions of different datasets or subgroups within a dataset.
• Summarizing Data: In boxplot visualizations, quartiles are used to summarize the data's spread in a visually intuitive manner.

### Quartiles in Boxplots

One of the most common uses of quartiles is in the creation of boxplots, also known as box-and-whisker plots. A boxplot is a standardized way of displaying the distribution of data based on a five-number summary: minimum, Q1, median (Q2), Q3, and maximum. The "box" shows the IQR, the "whiskers" extend to the smallest and largest values within 1.5*IQR from the quartiles (excluding outliers), and outliers are plotted as individual points.

### Conclusion

Quartiles are fundamental to understanding the distribution of data in statistics. They provide a concise summary of where data points lie in relation to one another and help identify trends, variability, and outliers. Whether used on their own or as part of a boxplot, quartiles are an essential tool for any data analyst or statistician.