How can we identify a suspected outlier using the interquartile range?

Using the Interquartile Rule to Find Outliers Calculate the interquartile range for the data. Multiply the interquartile range (IQR) by 1.5 (a constant used to discern outliers). Add 1.5 x (IQR) to the third quartile. Any number greater than this is a suspected outlier.

What does IQR tell you about outliers?

The IQR tells how spread out the “middle” values are; it can also be used to tell when some of the other values are “too far” from the central value. These “too far away” points are called “outliers”, because they “lie outside” the range in which we expect them.

How do you know if an outlier is an outlier?

You can convert extreme data points into z scores that tell you how many standard deviations away they are from the mean. If a value has a high enough or low enough z score, it can be considered an outlier. As a rule of thumb, values with a z score greater than 3 or less than –3 are often determined to be outliers.

What is Q1 1.5 * IQR?

The IQR, or more specifically, the zone between Q1 and Q3, by definition contains the middle 50% of the data. Extending that to 1.5*IQR above and below it is a very generous zone to encompass most of the data.

What is the 3 IQR rule?

The 3(IQR) criterion tells us that any observation that is below 3.5 or above 70 is considered an extreme outlier. We therefore conclude that the observations with ages 74 and 80 should be flagged as extreme outliers in the distribution of ages.

What is considered a good IQR?

Now, 1.5 times IQR is 6. Any values below 25, or higher than 41 will be considered outliers. Now, our friends with the ages 21, 57, and 64 are considered outliers.

What does IQR represent?

The IQR describes the middle 50% of values when ordered from lowest to highest. To find the interquartile range (IQR), ​first find the median (middle value) of the lower and upper half of the data. These values are quartile 1 (Q1) and quartile 3 (Q3). The IQR is the difference between Q3 and Q1.

What is the 1.5 XIQR rule?

A commonly used rule says that a data point is an outlier if it is more than 1.5 ⋅ IQR 1.5\cdot \text{IQR} 1. 5⋅IQR1, point, 5, dot, start text, I, Q, R, end text above the third quartile or below the first quartile.

Why is 1.5 IQR rule?

When scale is taken as 1.5, then according to IQR Method any data which lies beyond 2.7σ from the mean (μ), on either side, shall be considered as outlier. And this decision range is the closest to what Gaussian Distribution tells us, i.e., 3σ.