Outlier

outlier is a point for which $y_i$ is far from the value predicted by the model.

B) Outlier 판단 하기

B.1) Using standardization

A standard cut-off value for finding outliers are Z-scores of +/-3 or further from zero.
The probability distribution below displays the distribution of Z-scores in a standard normal distribution.

Z-scores beyond +/- 3 are so extreme you can barely see the shading under the curve.

B.1.1) 문제점

Z-scores can be misleading with small datasets because the maximum Z-score is limited to $\frac{n-1}{\sqrt{n}}$.
$n=10$ 이면, Z-score 는 3 을 넘지 못한다.

B.2) Using Boxplot

boxplots display asterisks or other symbols on the graph to indicate explicitly when datasets contain outliers.

These graphs use the interquartile method with fences to find outliers.

• The boxplot below shows a different dataset that has an outlier in the Method 2 group.

B.3) Using Residual Plot

• residual plots can be used to identify outliers.

C) Related

D) References