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Interquartile range (IQR)
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- Instructions: Use this outlier calculator by entering your sample data. This calculator will show you all the steps to apply the '1.5 x IQR' rule to detect outliers. These outliers will be shown in a box plot.
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How to enter data as a frequency table?
Simple. First-type data elements (separated by spaces or commas, etc.), then type f: and further write frequency of each data item. Each element must have defined frequency that count of numbers before and after symbol f: must be equal. For example:1.1 2.5 3.99
f: 5 10 15
How to enter a grouped data?
Grouped data are data formed by aggregating individual data into groups so that a frequency distribution of these groups serves as a convenient means of summarizing or analyzing the data.| group | frequency |
| 10-20 | 5 |
| 20-30 | 10 |
| 30-40 | 15 |
10-20 20-30 30-40
f: 5 10 15
How to enter data as a cumulative frequency table?
Similar as frequency table, but instead f: type cf: in second line. For example:10 20 30 40 50 60 70 80
cf: 5 13 20 32 60 80 90 100
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The cumulative frequency is calculated by adding each frequency from a frequency distribution table to the sum of its predecessors. The last value will always be equal to the total for all observations since all frequencies will already have been added to the previous total.Iqr Generator Review
About Outlier Calculator
The Outlier Calculator is used to calculate the outliers of a set of numbers.
FAQ
An outlier in a distribution is a number that is more than 1.5 times the length of the box away from either the lower or upper quartiles. Specifically, if a number is less than Q1 – 1.5×IQR or greater than Q3 + 1.5×IQR, then it is an outlier.
In descriptive statistics, the interquartile range (IQR) is a measure of statistical dispersion, being equal to the difference between the third quartile (Q3) and first quartile (Q1), that is, IQR = Q3 – Q1.
The first quartile, also called the lower quartile, is equal to the data at the 25th percentile of the data. The third quartile, also called the upper quartile, is equal to the data at the 75th percentile of the data.
There are several different methods for calculating quartiles. This calculator uses a method described by Moore and McCabe to find quartile values. The same method is also used by the TI-83 to calculate quartile values. With this method, the first quartile is the median of the numbers below the median, and the third quartile is the median of the numbers above the median.
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