The Right Way to Detect Outliers - Outlier Labeling Rule (part 1)

I demonstrate arguably the most valid way to detect outliers in data that roughly correspond to a normal distribution: the outlier labeling rule. I also point out that using 2.2 rather than the more common 1.5 is more appropriate as a multiplier.

The formulae I use in the video are:

Upper = Q3 + (2.2 * (Q3 - Q1))

Lower = Q1 -- (2.2 * (Q3 - Q1))

The references in video are:

Tukey, J.W. (1977). Exploratory Data Analysis. Reading, MA: Addison-Wesley.

Hoaglin, D.C., Iglewicz, B., and Tukey, J.W. (1986). Performance of some resistant rules for outlier labeling, Journal of American Statistical Association, 81, 991-999.

Hoaglin, D. C., and Iglewicz, B. (1987), Fine tuning some resistant rules for outlier labeling, Journal of American Statistical Association, 82, 1147-1149.

"outliers statistics" "statistical outlier"

Видео The Right Way to Detect Outliers - Outlier Labeling Rule (part 1) канала how2stats