The FREQUENCY function in Excel plays a crucial role in statistical analysis by summarizing data into a frequency distribution. It enables users to categorize a range of values and count how many fall within specified bins, making it an essential tool for data analysis and visualization.

Syntax

FREQUENCY(data_array, bins_array)

• data_array: This is the range of numbers for which you want to count the frequencies.
• bins_array: This specifies the intervals (or bins) into which the data will be grouped.

Example #1

=FREQUENCY(A2:A10, B2:B5)

In this case, the function will count how many values in the range A2:A10 fall within each interval defined in B2:B5. For instance, if A2:A10 contains scores ranging from 50 to 95, and B2:B5 defines bins like 60, 70, 80, and 90, the output would be an array indicating how many scores fall below 60, between 60 and 70, and so on.

Example #2

=FREQUENCY({10,20,30,30,40}, {15,25,35})

Here, the frequencies of the dataset {10,20,30,30,40} are evaluated against the bins {15,25,35}. The output would indicate that there is 1 number below 15, 2 numbers between 15 and 25, and 2 between 25 and 35, resulting in the array {1, 2, 2}.

Example #3

=FREQUENCY(D2:D15, E2:E5)

This function analyzes the range D2:D15, which could represent ages of individuals, against the predefined age bins in E2:E5. If the bins are 18, 30, 45, and 60, the output will show the number of individuals within each age range, helping to visualize demographic distributions.

Error handling

• NUM! – This error occurs if the size of the bins_array is greater than the data_array. Ensure that the bin range is appropriate for the data you are analyzing.
• VALUE! – This error indicates that either the data_array or bins_array contains non-numeric values. Verify that both arrays consist of numeric data.
• N/A – This may happen if the bins_array doesn’t contain any numbers that match the entries in the data_array. Adjust your bins accordingly.

Conclusion

The FREQUENCY function is an invaluable tool for anyone involved in data analysis, as it simplifies the process of quantifying data distributions. By categorizing data effectively with bins, users can gain insights into their data subsets, aiding in informed decision-making and reporting. Proper use of this function can enhance the clarity and value of statistical presentations.