# Regression without a dependent variable

I want to construct a multivariate model to find outliers in the data. The data I have is similar to the iris data (without the Species data attribute, I only have the first 4 attributes)

> head(iris)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa


It seems like there are a few methods for multivariate outlier detection. The document is as in this link

1. Mahalanobis Distance
2. Cook’s Distance
3. Leverage Point
4. DFFITS

All of them seem to require building a regression line and I understand that regression implies dependent variable. However, how can I choose a dependent variable from my data given that it only has the first 4 numeric continuous columns?

## migrated from stackoverflow.comMar 17 '17 at 7:23

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• Do you have a distribution model for your data? – Roland Mar 16 '17 at 18:46
• You want to look at clustering, not regression. – Dan Slone Mar 16 '17 at 18:48
• Yeah, I thought it was a clustering problem. But I soon realised that I would have to manually choose which cluster is outlier in the end, which I think is not ideal. – Duy Bui Mar 17 '17 at 10:27