Interquartile range
The interquartile range is the difference between the third and first quartiles of a distribution, measuring the spread of its middle half.
Statistics Slam Dunk
The interquartile range is the difference between the third and first quartiles and spans the middle half of a distribution.
- How it is used
- Compares the central spread of NBA performance distributions through boxplots.
- How deep it goes
- Defines quartiles and the IQR, then interprets it as the box in practical boxplots without deriving quartile calculations.
- Examples
- Uses career win shares by draft-player background and standardized wins by points scored or allowed categories.
- What you can do after
- Evaluate/understand the middle 50% and apparent outliers in a boxplot when comparing groups.
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D3.js in Action, Third Edition
The interquartile range is the interval between the first and third quartiles containing the middle half of an ordered dataset.
- How it is used
- Highlights the middle half of salary distributions in box plots and violin plots.
- How deep it goes
- Defines the first-to-third-quartile span and implements it through quantile scales and SVG positions.
- Examples
- Uses US salary distributions across five data-visualization roles, with IQR lines and mean dots on violin plots.
- What you can do after
- Build IQR markers in D3 box plots and violin plots from calculated quartile positions.
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Effective Data Analysis
The interquartile range is the spread of the middle 50 percent of observations between the 25th and 75th percentiles.
- How it is used
- Uses the IQR to summarize central variability and communicate dataset spread alongside other descriptive statistics.
- How deep it goes
- Defines percentile-based spread, compares it with range, and recommends visual inspection through boxplots without deriving calculations.
- Examples
- Uses race times, test scores, website visits, sleep data, and boxplots to illustrate central and overall spread.
- What you can do after
- Evaluate central spread, compare it with overall range, and report it responsibly for skewed or asymmetrical data.
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Cyber Threat Hunting
The interquartile range is the difference between the third and first quartiles of a data set, representing the spread of its middle half.
- How it is used
- Filters connection-timing outliers before variance and standard-deviation analysis for jittered C2 beacon hunting.
- How deep it goes
- Defines quartiles and demonstrates pandas filtering, then defers final judgments to endpoint and network investigation.
- Examples
- Uses time_diff_sec for Dropbox TCP/443 connections, then examines PowerShell and Dropbox.exe processes on two endpoints.
- What you can do after
- Apply it via pandas to filter connection intervals and prioritize low-variation patterns for investigation.
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Grokking Statistics
The difference between the third and first quartiles, representing the spread of the middle half of observations.
- How it is used
- Summarizes middle-half spread alongside the median when describing customer purchases or skewed tornado widths.
- How deep it goes
- Defines IQR through quartiles, calculates it in NumPy and pandas, and notes percentile interpolation for small datasets.
- Examples
- Uses bar-tab values with an IQR of 6 and NOAA tornado widths ranging from 50 to 250 yards for the middle half.
- What you can do after
- Apply it via NumPy and pandas to describe robust spread and compare grouped numeric data.
Pandas Workout: 200 exercises to make you a stronger data analyst
The interquartile range is the difference between the 75th-percentile and 25th-percentile values of an ordered dataset.
- How it is used
- Uses IQR to identify unusual observations and compare their associated measurements with the full dataset.
- How deep it goes
- Defines IQR, derives percentile thresholds, filters rows, and visualizes results with boxplots without developing alternative statistical theory.
- Examples
- Works through taxi distances and passenger counts, then examines Chicago temperature outliers with boxplots.
- What you can do after
- Apply via pandas by calculating quantiles, filtering IQR-based outliers, and comparing selected rows with the full dataset.
Outlier Detection in Python
The interquartile range is a measure of statistical dispersion equal to the difference between the third and first quartiles of a distribution.
- How it is used
- Identifies extreme values in numeric columns and provides a quick screening step before broader outlier detection.
- How deep it goes
- Explains quartile calculation, threshold formulas, coefficient choices, implementation, and univariate limitations.
- Examples
- Uses pandas and NumPy thresholds, Gaussian distribution figures, boxplots, and ADTK detection on Twitter time-series data.
- What you can do after
- Apply it via pandas or ADTK to calculate robust thresholds and flag candidate extreme values.
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Related concepts
- standard deviationalternative to · Standard deviation is the nonnegative square root of variance and measures the typical dispersion of values around their mean in the original units.
- anomalyused by · An anomaly is an observation, behavior, or state that deviates from an expected or typical pattern or condition.
- RobustScalerused by · RobustScaler is a scikit-learn transformer that scales features using robust statistics such as the median and interquartile range.
- outlier detectionused by · Outlier detection is the process of identifying observations that substantially deviate from an expected or prevailing pattern in data.
- quantilesuses · A quantile is a value that divides an ordered dataset or probability distribution so that a specified proportion of observations lies at or below it.
- upper quartileuses · The upper quartile, or third quartile, is the value marking approximately the 75th percentile of an ordered dataset.
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