If you want to know the mathematics used to identify outliers, let's begin by talking about quartiles, which divide a data set into quarters:

However, for those situations where removing outliers is worthwhile, you can first highlight outliers per the Conditional Formatting steps above, then right-click on the column again and use Subset Worksheet > Exclude Rows with Formatted Cells to create the new data set. After all, they may have a story вЂ“ perhaps a very important story вЂ“ to tell. Now IвЂ™m not suggesting that removing outliers should be done without thoughtful consideration. If you then want to create a new data set that excludes these outliers, thatвЂ™s easy to do too. Each outlier in your worksheet will then be highlighted in red, or whatever color you choose. To highlight outliers directly in the worksheet, you can right-click on your column of data and choose Conditional Formatting > Statistical > Outlier. Boxplots are certainly one of the most common ways to visually identify outliers, but there are other graphs, such as scatterplots and individual value plots, to consider as well. This boxplot shows a few outliers, each marked with an asterisk. If you want to identify them graphically and visualize where your outliers are located compared to rest of your data, you can use Graph > Boxplot. Of course, you have to find them first.ДЇinding outliers in a data set is easy using Minitab Statistical Software, and there are a few ways to go about it. Outliers can provide useful information about your data or process, so it's important to investigate them. These unusual observations can have a disproportionate effect on statistical analysis, such as the mean, which can lead to misleading results. An outlier is an observation in a data set that lies a substantial distance from other observations.

Scilab identify outlier