The interquartile range, or IQR for short, is an important way to understand how spread out the numbers are in a group. It also helps us find any unusual values, called outliers.
Here’s a simple breakdown:
What is IQR?
We find the IQR using this formula:
IQR = Q3 - Q1
Here, Q1 is the first quartile (the middle value of the lower half of the data), and Q3 is the third quartile (the middle value of the upper half).
Finding Outliers:
A number is seen as an outlier if it falls outside this range:
[Q1 - 1.5 × IQR, Q3 + 1.5 × IQR]
This means we look for data points that are much lower or much higher than most of the others.
Example:
Let’s say Q1 is 10 and Q3 is 20.
We can find the IQR like this:
IQR = 20 - 10 = 10
Now, to find the outlier boundaries, we do this:
[10 - 15, 20 + 15], which gives us:
[-5, 35]
Any data point below -5 or above 35 is considered an outlier.
So, the IQR helps us figure out which numbers are normal and which ones are odd or unusual.
The interquartile range, or IQR for short, is an important way to understand how spread out the numbers are in a group. It also helps us find any unusual values, called outliers.
Here’s a simple breakdown:
What is IQR?
We find the IQR using this formula:
IQR = Q3 - Q1
Here, Q1 is the first quartile (the middle value of the lower half of the data), and Q3 is the third quartile (the middle value of the upper half).
Finding Outliers:
A number is seen as an outlier if it falls outside this range:
[Q1 - 1.5 × IQR, Q3 + 1.5 × IQR]
This means we look for data points that are much lower or much higher than most of the others.
Example:
Let’s say Q1 is 10 and Q3 is 20.
We can find the IQR like this:
IQR = 20 - 10 = 10
Now, to find the outlier boundaries, we do this:
[10 - 15, 20 + 15], which gives us:
[-5, 35]
Any data point below -5 or above 35 is considered an outlier.
So, the IQR helps us figure out which numbers are normal and which ones are odd or unusual.