Quartile
A quartile is a quantile or cut point that divides ordered observations into four approximately equal parts.
The Well-Grounded Data Analyst
A quartile is one of the values dividing an ordered dataset into four equal parts.
- How it is used
- Guides traffic-volume cutoff choices when selecting locations for further analysis.
- How deep it goes
- Applies quartile outputs to a skewed distribution without detailing their calculation.
- Examples
- Uses total bicycle counts across locations, including a 216-count median threshold.
- What you can do after
- Apply quartiles to justify a practical threshold for prioritizing locations.
Statistics Slam Dunk
A quartile is a positional statistic that divides an ordered distribution into four equal-probability sections.
- How it is used
- Uses quartiles to summarize NBA win-share distributions and interpret grouped boxplots.
- How deep it goes
- Defines Q1, Q3, IQR, whiskers, and outliers through boxplot interpretation without deriving quartile algorithms.
- Examples
- Uses NBA first-round selections, career win shares, grouped boxplots, and R summary output.
- What you can do after
- Apply it via R and ggplot2 to summarize distributions and interpret boxplots.
Statistics Every Programmer Needs
A quartile is a quantile that divides ordered observations into four equal parts.
- How it is used
- Summarizes the distribution of numeric columns for initial data exploration.
- How deep it goes
- Defines three percentile cut points and demonstrates their computed output, but does not derive the calculation algorithm.
- Examples
- Uses pandas describe() on the 20-record mds dataset, reporting quartiles for stage1 and stage2.
- What you can do after
- Apply quartile summaries through pandas describe() when inspecting numeric data.
Read how Statistics Every Programmer Needs explains quartile →
Cyber Threat Hunting
A quartile is a value that divides an ordered dataset into four ranges containing approximately equal numbers of observations.
- How it is used
- Uses quartiles to reduce outlier noise before measuring variability in adversary-related connection timing data.
- 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 you can do after
- Apply it via pandas to remove timing values outside interquartile bounds before calculating grouped variability.
Related concepts
- percentileincludes · A percentile is a value in an ordered distribution at or below which a specified proportion of observations falls.
- upper quartileincludes · The upper quartile, or third quartile, is the value marking approximately the 75th percentile of an ordered dataset.
- quantileskind of · 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.
- thresholdconfigures · A threshold is a boundary value against which a quantity is compared to determine a decision, classification, or action.
- interquartile rangemeasured by · The interquartile range is the difference between the third and first quartiles of a distribution, measuring the spread of its middle half.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.