The Right Way to Detect Outliers - Outlier Labeling Rule (part 1)
Автор: how2stats
Загружено: 2011-09-08
Просмотров: 239333
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"
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