liveBook · Manning

Quartile

As explained in Cyber Threat Hunting

A quartile is a value that divides an ordered dataset into four ranges containing approximately equal numbers of observations.

A quartile is a value that divides an ordered dataset into four ranges containing approximately 25% of the observations each. The lower quartile marks the point below which 25% of values fall, the median divides the data in half, and the upper quartile marks the point below which 75% fall. section 7.1.2

Quartiles support interquartile-range filtering when data is skewed or contains outliers. Keeping values between the lower and upper quartiles reduces noise before calculating variance and standard deviation. section 7.1.2

Filtering connection timing data

For grouped network events, calculate lower and upper quartiles for time_diff_sec, drop rows outside those bounds, and then compute grouped standard deviation, variance, and connection counts. section 7.1.2

Figure 7.4 Quartiles for a nonnormal distribution

Use the quartile box plot to inspect quartile positions in a nonnormal distribution. section 7.1.2

How Cyber Threat Hunting covers it

How deep it goes
Defines three quartiles and applies their bounds to filtering, but defers detailed calculation mechanics.
Examples
Uses time_diff_sec distributions, nonnormal and left-skewed box plots, and grouped network-event filtering listings.
What it assumes
Assumes ordered numerical data and teaches the median and quartile positions directly.
What you can do after
Apply it via pandas to remove timing values outside interquartile bounds before calculating grouped variability.
How it connects
Connects quartiles with interquartile range, median, outliers, skewed data, variance, standard deviation, and box plots.

Also explained in

Compare how 4 books explain quartile →

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Cyber Threat Hunting. The section links point to the parts of the book it draws on.