Multivariate outlier detection in r

Outliers in data can distort predictions and affect the accuracy, if you don’t detect and handle them appropriately especially in regression models.

Why outliers detection is important?

Treating or altering the outlier/extreme values in genuine observations is not a standard operating procedure.

However, it is essential to understand their impact on your predictive models. It is left to the best judgement of the investigator to decide whether treating outliers is necessary and how to go about it.

So, why identifying the extreme values is important?

How to find outlier in r software

Because, it can drastically bias/change the fit estimates and predictions. Let me illustrate this using the cars dataset.

To better understand the implications of outliers better, I am going to compare the fit of a simple linear regression model on cars dataset with and without outliers.

In order to distinguish the effect clearly, I manually introduce extreme values to the original cars dataset. Then, I predict on both the datasets.

Notice the change in slope of the best fit line after removing the outlie

how to find outliers in residual plot

how to find outliers in regression