This notebook demonstrates a common workflow for detecting and removing outliers from a dataset using the Interquartile Range (IQR) method. Outliers are data points that significantly deviate from the ...

and how to apply it in Python. IQR is a measure of how spread out the middle 50% of the data is. It is calculated by subtracting the first quartile (Q1) from the third quartile (Q3). The first ...

Um método comum é o método IQR, que significa intervalo interquartil. Neste artigo, você aprenderá o que é o método IQR, como ele funciona e como aplicá-lo em Python. O IQR é uma medida ...

Step 1: Order Your Data The first step in calculating the IQR is to arrange your data in ascending order, from the smallest value to the largest value. Step 2: Find the Quartiles Next, you’ll need to ...

The \(\frac{3{(n+1)}}{4}\) value So the upper quartile (UQ) is 18. The interquartile range (IQR) is therefore 18 - 4 = 14. You will notice that the fact there is an outlier in this data (60 ...