Continuous data
Continuous data consists of numerical measurements that can take arbitrarily fine-grained values across a range or interval.
Statistics Slam Dunk
Continuous data consists of numeric observations that can take values across a range, including fractional values.
- How it is used
- Supports visualization, correlation, significance testing, and regression questions involving numeric measurements.
- How deep it goes
- Defines continuous variables and works through correlations, linear models, tests, and numeric-to-factor preparation without formal proofs.
- Examples
- Uses automobile performance measures, NBA draft statistics, team foul calls, free throws, points, and wins.
- What you can do after
- Apply it via R to select numeric variables, compute correlations, visualize distributions, test group differences, and model continuous targets.
D3.js in Action, Third Edition
A continuous value can take any value within a specified interval or spectrum.
- How it is used
- Guides visualization-channel and D3-scale choices for measured quantitative values.
- How deep it goes
- Defines interval-based values and applies them to linear and sequential scales, without developing scale internals.
- Examples
- Uses temperature, salaries, TV review scores, and Nobel laureate counts as continuous-value visualization examples.
- What you can do after
- Apply it via D3 scales and visual channels to represent measured values as positions, lengths, areas, angles, or colors.
Read how D3.js in Action, Third Edition explains continuous data →
Everyday Data Visualization
A data dimension whose values may take any position or value along a continuous spectrum or interval.
- How it is used
- Guides visualization choices by distinguishing values that form a continuum from values that remain separate categories.
- How deep it goes
- Defines continuity, explains scale-dependent cases, and connects it to chart perception without prescribing rigid chart boundaries.
- Examples
- Uses fruit taste scores, fruit identifiers, dates, times, datetimes, and seasonal taste over time as examples.
- What you can do after
- Evaluate whether a dataset supports connected or separated marks and choose an understandable chart accordingly.
Read how Everyday Data Visualization explains continuous data →
The Well-Grounded Data Analyst
Continuous data consists of numerical measurements that can take values across a range, including arbitrarily fine subdivisions.
- How it is used
- Classifies tabular columns before analysis and separates numerical measurements from categorical values.
- How deep it goes
- Distinguishes interval and ratio scales through spacing and zero meaning, but stops short of detailed statistical modeling.
- Examples
- Uses age, temperature, height, weight, and patient measurements, including data from a heart disease dataset.
- What you can do after
- Evaluate whether a column is continuous and choose an interval or ratio interpretation before analysis.
Read how The Well-Grounded Data Analyst explains continuous data →
Data Without Labels
Continuous data can take any value within a range, including fractional or decimal values.
- How it is used
- Classifies measured business variables whose values may vary fractionally within a range.
- How deep it goes
- Defines continuous data and contrasts it with discrete data, stopping short of modeling procedures or implementation workflows.
- Examples
- Uses height and product price as continuous examples, contrasting passenger counts and city populations as discrete data.
- What you can do after
- Evaluate whether a measured variable is continuous or discrete based on whether fractional values are meaningful.
Related concepts
- discrete variablecontrasts with · A discrete variable is a variable whose possible values belong to a finite or countable set of distinct alternatives rather than a continuous range.
- discretizationused by · Discretization is the process of mapping values or observations into a finite set of discrete intervals or categories.
- line plotused by · A line plot is a chart that connects ordered data values with line segments to show changes, relationships, or trends.
- categorical datacontrasts with · Categorical data consists of values representing membership in a discrete set of named or ordered categories rather than measured numerical magnitudes.
- quantitative datakind of · Quantitative data is information represented numerically as quantities, measurements, counts, or other values suitable for numerical interpretation or analysis.
- datakind of · Data is represented information, observations, measurements, or other values that can be stored, processed, transmitted, analyzed, or interpreted.
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