![]() ![]() Steve Simon while working at Children's Mercy Hospital. ![]() Observations (4.80 and 5.26) and halfway between the 3rd and 4th observations 75*(7+1) = 2nd and 6th observations, respectively.ģ.25 and 11.75 respectively. The IQR is also equal to the length of the box in a box plot.Ĭompute the interquartile range for the sorted Cotinine data: Middle 50%, the IQR is not affected by outliers or extreme values. The IQR isĮssentially the range of the middle 50% of the data. Notice how the range changes dramatically as a result of one outlier.īefore calculating the range of any dataset, it’s a good idea to first check if there are any outliers that could cause the range to be misleading.The interquartile range (IQR) is the distance between theħ5 th percentile and the 25 th percentile. The range of this dataset would now be 378 – 1 = 377. ![]() The range of this dataset is 32 – 1 = 31. To illustrate this, consider the following dataset: The range suffers from one drawback: It is influenced by outliers. We can use both metrics since they provide us with completely different information. It’s worth noting that we don’t have to choose between using the range or the interquartile range to describe the spread of values in a dataset. She can use the range to understand the difference between the highest score and the lowest score received by all of the students in the class.Ĭonversely, we should use the interquartile range when we’re interested in understanding the spread between the 75th percentile and 25th percentile of a dataset.įor example, if a professor administers an exam to 100 students, she can use the interquartile range to quickly understand the difference in exam score between a student who scored at the 75th percentile of scores and a student who scored at the 25th percentile. We should use the range when we’re interested in understanding the difference between the largest and smallest values in a dataset.įor example, suppose a professor administers an exam to 100 students. The interquartile range tells us the spread of the middle 50% of values in the dataset.The range tells us the difference between the largest and smallest value in the entire dataset.However, the range and interquartile range have the following difference: Both metrics measure the spread of values in a dataset.The range and interquartile range share the following similarity: Interquartile Range: Similarities & Differences The range tells us the spread of the entire dataset while the interquartile range tells us the spread of the middle half of the dataset. Interquartile Range = 3rd Quartile – 1st Quartile.We can use the Interquartile Range Calculator to help us calculate the interquartile range: We can use the following steps to calculate the range: This represents the spread of the middle 50% of values. The interquartile range measures the difference between the first quartile (25th percentile) and third quartile (75th percentile) in a dataset. ![]() The range measures the difference between the minimum value and the maximum value in a dataset. In statistics, the range and interquartile range are two ways to measure the spread of values in a dataset. ![]()
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