Interquartile range (IQR) is a critical statistical measure that quantifies the dispersion of a dataset. Calculating IQR in Excel involves a series of steps including identifying the quartiles, which represents a core skill for data analysts and researchers.
This guide provides a straightforward approach to compute IQR using Excel functions. However, the final section will introduce Sourcetable, highlighting its user-friendly interface that simplifies the process compared to the conventional Excel method.
To calculate the Interquartile Range (IQR) in Excel, one must understand that there is no direct IQR function, but the IQR can be calculated using the QUARTILE
, QUARTILE.INC
, or QUARTILE.EXC
functions. The IQR represents the middle dispersion of a dataset, highlighting the range within the middle 50% of the data points.
The QUARTILE
function in Excel calculates quartiles and can determine the IQR using the inclusive method. It requires an array of data points and the specific quartile to calculate. To find the IQR, the first and third quartiles (Q1 and Q3) are needed, and these can be obtained by setting the second argument to 1 for Q1 using =QUARTILE(array, 1)
, and to 3 for Q3 using =QUARTILE(array, 3)
. The IQR is the result of subtracting Q1 from Q3.
The process to find the IQR in Excel includes entering data into the sheet, finding Q1 and Q3, and subtracting the former from the latter. Specifically, follow these steps:
QUARTILE
function to determine the first quartile (Q1) with the formula =QUARTILE(data_array, 1)
.=QUARTILE(data_array, 3)
.=QUARTILE(data_array, 3) - QUARTILE(data_array, 1)
.QUARTILE
function to determine the first quartile (Q1) with the formula =QUARTILE(data_array, 1)
.=QUARTILE(data_array, 3)
.=QUARTILE(data_array, 3) - QUARTILE(data_array, 1)
.This calculation is useful across fields such as statistics, finance, medicine, insurance, and more for analyzing data sets and excluding outliers.
For more flexibility with dataset methodology, the QUARTILE.INC
and QUARTILE.EXC
functions are alternatives that allow specifying exclusive or inclusive methods. They also require the data set and quartile number as arguments, similar to the main QUARTILE function.
Analyzing the spread of data for quality control assessments
Identifying outliers in a dataset of annual sales figures
Assessing wage distribution fairness within a company
Comparing the variability of test scores between different classrooms
Evaluating the consistency of response times in customer service data
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