The Interquartile Range (IQR) is a measure of variation that describes how spread out the data is. It is the difference between the first quartile (Q1) and the third quartile (Q3). The middle half of the data lies between Q1 and Q3.
– Q1: The value that separates the bottom 25% of the data from the top 75%.
– Q3: The value that separates the bottom 75% of the data from the top 25%.
Let’s consider the ages of Nobel Prize winners: 13, 21, 21, 40, 42, 48, 55, 72.
– Q1: 21
– Q3: 55
– Interquartile Range (IQR): 55 – 21 = 34
Programming languages like Python and R make IQR calculations efficient and accurate. Here’s how:
from scipy import stats
values = [13, 21, 21, 40, 42, 48, 55, 72]
x = stats.iqr(values)
print(x)
values <- c(13, 21, 21, 40, 42, 48, 55, 72)
IQR(values)
The Interquartile Range is a powerful tool to understand the spread of data. With programming languages like Python and R, it is easy to calculate IQR for large datasets, making data analysis faster and more efficient.