The IQR is 48. But for only 6 observations, it is an absurd measure to use.
No. The IQR is found by finding the lower quartile, then the upper quartile. You then minus the lower quartile value from the upper quartile value (hence "interquartile"). This gives you the IQR.
No. The IQR is a resistant measurement.
No, it is not possible.
The exact definition of which points are considered to be outliers is up to the experimenters. A simple way to define an outlier is by using the lower (LQ) and upper (UQ) quartiles and the interquartile range (IQR); for example: Define two boundaries b1 and b2 at each end of the data: b1 = LQ - 1.5 × IQR and UQ + 1.5 × IQR b2 = LQ - 3 × IQR and UQ + 3 × IQR If a data point occurs between b1 and b2 it can be defined as a mild outlier If a data point occurs beyond b2 it can be defined as an extreme outlier. The multipliers of the IQR for the boundaries, and the number of boundaries, can be adjusted depending upon what definitions are required/make sense.
No.
The IQR is 7.5
IQR = Inter-Quartile Range = Upper Quartile - Lower Quartile.
IQR = Inter Quartile RangeIQR = Inter Quartile RangeIQR = Inter Quartile RangeIQR = Inter Quartile Range
An interquartile range is a measurement of dispersion about the mean. The lower the IQR, the more the data is bunched up around the mean. It's calculated by subtracting Q1 from Q3.
The IQR is 48. But for only 6 observations, it is an absurd measure to use.
No. The IQR is found by finding the lower quartile, then the upper quartile. You then minus the lower quartile value from the upper quartile value (hence "interquartile"). This gives you the IQR.
The IQR gives the range of the middle half of the data and, in that respect, it is a measure of the variability of the data.
Because the IQR excludes values which are lower than the lower quartile as well as the values in the upper quartile.
No. The IQR is a resistant measurement.
No, it is not possible.
The exact definition of which points are considered to be outliers is up to the experimenters. A simple way to define an outlier is by using the lower (LQ) and upper (UQ) quartiles and the interquartile range (IQR); for example: Define two boundaries b1 and b2 at each end of the data: b1 = LQ - 1.5 × IQR and UQ + 1.5 × IQR b2 = LQ - 3 × IQR and UQ + 3 × IQR If a data point occurs between b1 and b2 it can be defined as a mild outlier If a data point occurs beyond b2 it can be defined as an extreme outlier. The multipliers of the IQR for the boundaries, and the number of boundaries, can be adjusted depending upon what definitions are required/make sense.