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No. The upper quartile, by definition, must be at least as large as the lower quartile.

Q: Can IQR be negative

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IQR = Inter Quartile RangeIQR = Inter Quartile RangeIQR = Inter Quartile RangeIQR = Inter Quartile Range

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.

The interquartile range (IQR) is a measure of variability, based on dividing a data set into quartiles. Quartiles divide a rank-ordered data set into four equal parts.

Iqr stands for inter quartile range and it is used to find the middle of the quartiles in a set of data. To find this, you find the lower quartile range and the upper quartile range, and divide them both together.

Step 1: Find the upper quartile, Q3.Step 2: Find the lower quartile: Q1.Step 3: Calculate IQR = Q3 - Q1.Step 1: Find the upper quartile, Q3.Step 2: Find the lower quartile: Q1.Step 3: Calculate IQR = Q3 - Q1.Step 1: Find the upper quartile, Q3.Step 2: Find the lower quartile: Q1.Step 3: Calculate IQR = Q3 - Q1.Step 1: Find the upper quartile, Q3.Step 2: Find the lower quartile: Q1.Step 3: Calculate IQR = Q3 - Q1.

Related questions

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

The IQR is 48. But for only 6 observations, it is an absurd measure to use.

No.

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.

It gives a measure of the spread of the data.