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Quartile Deviation Calculator

Calculate the quartile deviation (semi-interquartile range) of a dataset with interactive box plot visualization, complete quartile analysis (Q1, Q2, Q3, IQR), outlier detection, and step-by-step calculation breakdown.

Quartile Deviation Calculator

Try these example datasets:

Simple Set Test Scores With Outlier Decimals

Separate numbers with comma, space, or line break. If your text has non-numeric content, use our Number Extractor first.

Embed Quartile Deviation Calculator Widget

About Quartile Deviation Calculator

The Quartile Deviation Calculator is a comprehensive statistical tool that calculates the quartile deviation (also known as the semi-interquartile range) of your dataset. This calculator provides a complete five-number summary, interactive box plot visualization, automatic outlier detection using the 1.5 IQR rule, and detailed step-by-step calculation breakdowns. Whether you are a student learning statistics, a researcher analyzing data, or a professional making data-driven decisions, this tool helps you understand the spread and distribution of your data.

What is Quartile Deviation?

Quartile deviation (QD), also called the semi-interquartile range (SIQR), is a measure of statistical dispersion that indicates how spread out the middle 50% of your data is. It is calculated as half the interquartile range (IQR):

Quartile Deviation Formula
$$QD = \frac{Q_3 - Q_1}{2} = \frac{IQR}{2}$$

Where:

Why Use Quartile Deviation?

Understanding Quartiles and IQR

The Three Quartiles

Quartiles divide a sorted dataset into four equal parts:

Interquartile Range (IQR)

The interquartile range is the difference between Q3 and Q1, representing the range of the middle 50% of data. It is a key measure of spread that forms the basis for quartile deviation and outlier detection.

Interquartile Range Formula
$$IQR = Q_3 - Q_1$$

The relationship between IQR and quartile deviation is simple: QD = IQR / 2. This means the quartile deviation represents the average spread from the median to each quartile boundary.

How to Use This Calculator

  1. Enter your data: Input numbers in the text area, separated by commas, spaces, or line breaks. The calculator accepts both integers and decimals, including negative numbers.
  2. Use example data (optional): Click any example button to load pre-set datasets demonstrating different scenarios like normal distributions, datasets with outliers, or test scores.
  3. Click Calculate: Press the "Calculate Quartile Deviation" button to process your data.
  4. Review the quartile summary: Examine Q1, Q2 (median), Q3, IQR, and the quartile deviation prominently displayed.
  5. Analyze the box plot: The interactive box plot visualizes your data distribution, showing quartiles, whiskers, and outliers.
  6. Check for outliers: The calculator automatically detects outliers using the 1.5 IQR rule.
  7. Study the step-by-step breakdown: Expand the detailed calculation section to understand exactly how each value was computed.

The Five-Number Summary

The five-number summary provides a complete picture of your data distribution:

Statistic Description Percentile
Minimum Smallest value in the dataset 0th
Q1 (First Quartile) Median of lower half 25th
Q2 (Median) Middle value 50th
Q3 (Third Quartile) Median of upper half 75th
Maximum Largest value in the dataset 100th

Outlier Detection with IQR

This calculator uses the 1.5 IQR rule (Tukey's method) to detect outliers:

The calculator distinguishes between:

Quartile Deviation vs Standard Deviation

Aspect Quartile Deviation Standard Deviation
Calculation basis Uses Q1 and Q3 only Uses all data points
Outlier sensitivity Robust (not affected) Sensitive (heavily affected)
Best for Skewed or ordinal data Normal distributions
Interpretation Average distance to quartiles Average distance to mean
Normal distribution relationship QD approximately equals 0.67 times SD SD approximately equals 1.5 times QD

Coefficient of Quartile Deviation

The coefficient of quartile deviation (CQD) is a relative measure of dispersion that allows comparison between datasets with different units or scales:

Coefficient of Quartile Deviation
$$CQD = \frac{Q_3 - Q_1}{Q_3 + Q_1} \times 100\%$$

The CQD is useful when comparing variability across datasets with different means or units. A higher CQD indicates greater relative dispersion.

Real-World Applications

Education and Testing

Quartile deviation helps educators understand score distributions. A small QD indicates students performed similarly, while a large QD suggests wide variation in performance.

Quality Control

Manufacturing uses quartile deviation to assess product consistency. Products with low QD have more uniform specifications.

Finance and Economics

Financial analysts use QD to measure income inequality, price stability, and investment risk in ways that are not skewed by extreme values.

Healthcare

Medical researchers use quartile-based statistics to analyze patient data, treatment outcomes, and biological measurements that may not be normally distributed.

Social Sciences

Survey data often has ordinal scales where quartile deviation is more appropriate than standard deviation for measuring spread.

Step-by-Step Calculation Example

For the dataset: 2, 4, 6, 8, 10, 12, 14, 16, 18

  1. Sort the data: 2, 4, 6, 8, 10, 12, 14, 16, 18 (n = 9)
  2. Find Q2 (Median): Middle value = x5 = 10
  3. Find Q1: Median of lower half (2, 4, 6, 8) = (4 + 6) / 2 = 5
  4. Find Q3: Median of upper half (12, 14, 16, 18) = (14 + 16) / 2 = 15
  5. Calculate IQR: 15 - 5 = 10
  6. Calculate QD: 10 / 2 = 5

The quartile deviation of 5 means that on average, values in the middle 50% of the data are within 5 units of the median.

Frequently Asked Questions

What is quartile deviation?

Quartile deviation (QD), also known as semi-interquartile range (SIQR), is a measure of statistical dispersion equal to half the interquartile range (IQR). It is calculated as QD = (Q3 - Q1) / 2, where Q3 is the third quartile (75th percentile) and Q1 is the first quartile (25th percentile). Quartile deviation measures the spread of the middle 50% of data and is robust to outliers.

How do you calculate quartile deviation step by step?

To calculate quartile deviation: 1) Sort your data in ascending order. 2) Find Q1 (first quartile) - the median of the lower half of data. 3) Find Q3 (third quartile) - the median of the upper half of data. 4) Calculate IQR = Q3 - Q1. 5) Calculate QD = IQR / 2. For example, with data 2, 4, 6, 8, 10, 12, 14: Q1 = 4, Q3 = 12, IQR = 8, QD = 4.

What is the difference between quartile deviation and standard deviation?

Quartile deviation and standard deviation both measure data spread, but differ in key ways. Quartile deviation uses quartiles (Q1 and Q3) and is robust to outliers, making it ideal for skewed data. Standard deviation uses all data points and squares the differences from the mean, making it sensitive to outliers. For normally distributed data, standard deviation is approximately 1.5 times the quartile deviation.

What is the interquartile range (IQR)?

The interquartile range (IQR) is the difference between the third quartile (Q3) and first quartile (Q1), representing the range of the middle 50% of data. IQR = Q3 - Q1. The IQR is twice the quartile deviation. It is commonly used for outlier detection: values below Q1 - 1.5 times IQR or above Q3 + 1.5 times IQR are considered potential outliers.

What is the coefficient of quartile deviation?

The coefficient of quartile deviation (CQD), also called the quartile coefficient of dispersion, is a relative measure of variability that allows comparison between datasets with different units or scales. It is calculated as CQD = (Q3 - Q1) / (Q3 + Q1) times 100. The result is expressed as a percentage, with higher values indicating greater relative dispersion.

Additional Resources

To learn more about quartile deviation and statistical measures of dispersion:

Reference this content, page, or tool as:

"Quartile Deviation Calculator" at https://MiniWebtool.com/quartile-deviation-calculator/ from MiniWebtool, https://MiniWebtool.com/

by miniwebtool team. Updated: Jan 05, 2026

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