In my previous article, we see the outlier detection technique univariate approach, let’s look further

Statistical Techniques and tools

2.1 Standardized Residuals

2.2 Studentized Residuals

2.3 COOK’S Distance

2.4 Leverage

2.5 DFBETAS

**2.6 **DFFITS

2.1 Standardized Residuals

Since the approximate average variance of a residual is estimated by MSRes, a

logical scaling for the residuals would be the standardized residuals. The standardized residuals have mean zero and approximately unit variance.

Criteria:

A large standardized residual (di > 3) potentially indicates an outlier.

2.2 Studentized Residuals

A studentized residual (sometimes referred to as an “externally studentized

residual” or a “deleted t residual”) is:

Criteria :

Studentized residuals are going to be more effective for detecting outlying

observations than standardized residuals. If an observation has a studentized residual that is larger than 3 (in absolute value) we can call it an outlier.

2.3 COOK’S Distance

Its formula is given as,

Criteria :

We usually consider points for which Di>1 We can call i th observation is an outlier.

#r #outliers #clinical-trials #machine-learning #data-science