If the result is 1.5 x Inter Quartile Range (or more) above the Upper Quartile or 1.5 x Inter Quartile Range (or more) below the Lower Quartile.
no
None - as long as the ouliers move away from the median - which they should.
The whiskers mark the ends of the range of figures - they are the furthest outliers. * * * * * No. Outliers are not part of a box and whiskers plot. The whiskers mark the ends of the minimum and maximum observations EXCLUDING outliers. Outliers, if any, are marked with an X.
Yes, it is possible to have two or more outliers in a dataset. Outliers are values that significantly differ from the rest of the data, and multiple values can meet this criterion. The presence of multiple outliers can indicate variability in the data or suggest underlying issues like measurement errors or unusual occurrences. Analyzing these outliers can provide valuable insights into the data's characteristics.
You can only do it if either the outliers are way out - so far that they must be odd, so far that there can be no argument, no need for statistics to prove them to be outliers, or you need to prove that they are outliers using statistics - something like Grubb's test. To do that, the simplest way is software.
no
there are no limits to outliers there are no limits to outliers
None - as long as the ouliers move away from the median - which they should.
The ISBN of Outliers - book - is 9780316017923.
"Outliers" by Malcolm Gladwell has approximately 320 pages in its paperback edition.
There is no limit to the number of outliers there can be in a set of data.
Outliers - book - was created on 2008-11-18.
apparently there is no limit to outliers. at least according to everybody else's answers.
The whiskers mark the ends of the range of figures - they are the furthest outliers. * * * * * No. Outliers are not part of a box and whiskers plot. The whiskers mark the ends of the minimum and maximum observations EXCLUDING outliers. Outliers, if any, are marked with an X.
Outliers - 2010 was released on: USA: 5 February 2010
The assumption in the question is not valid. There are two main reasons for outliers: one is that there is a measurement or recording error and if that is the case, then the outlier should be excluded. However, it is possible that the model under consideration is no longer valid when you get near the outlier.
You can only do it if either the outliers are way out - so far that they must be odd, so far that there can be no argument, no need for statistics to prove them to be outliers, or you need to prove that they are outliers using statistics - something like Grubb's test. To do that, the simplest way is software.