Discrete variable
A discrete variable is a variable whose possible values belong to a finite or countable set of distinct alternatives rather than a continuous range.
D3.js in Action, Third Edition
A discrete value belongs to a predetermined finite or otherwise distinct set of possible values.
- How it is used
- Guides data classification, visual encoding, and D3 scale selection for charts.
- How deep it goes
- Defines countable whole numbers, contrasts them with continuous measurements, and extends the idea to discrete scale inputs and outputs.
- Examples
- Uses employee counts, coffee sizes, TV genres, and gender categories as discrete values in charts and scales.
- What you can do after
- Apply it via D3 scales to choose discrete domains, categorical outputs, or continuous positions for chart variables.
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Everyday Data Visualization
A data dimension whose values are separate, distinct, and individually identifiable rather than varying continuously across a range.
- How it is used
- Guides visualization choices by distinguishing separate values from values that vary along a continuum.
- How deep it goes
- Explains definitions, scale-dependent dates, perceptual rationale, and chart choices without formal mathematical treatment.
- Examples
- Uses fruit identifiers, taste scores, seasonal dates, and a line-versus-bar chart comparison.
- What you can do after
- Apply chart-selection guidance by recognizing discrete values and choosing bar charts when separated categories need comparison.
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Causal AI
A discrete outcome is one of a finite or countable set of distinct values that a discrete random variable can take.
- How it is used
- Frames finite-outcome uncertainty for probability models and causal reasoning without depending on discrete or continuous parameterization.
- How deep it goes
- Defines outcomes, notation, probability masses, and distribution classes, but stops short of deeper distribution theory.
- Examples
- Uses three-sided dice, coin flips, categorical food choices, and indexed categories as concrete discrete outcomes.
- What you can do after
- Apply discrete variables by specifying outcomes, assigning probability masses, and selecting categorical or Bernoulli distributions.
Data Without Labels
Discrete data consists of distinct countable values, typically represented as integers.
- How it is used
- Classifies count-based measurements before analysis and model building.
- How deep it goes
- Defines integer-valued counts and contrasts them with continuous, nominal, and ordinal data without developing modeling procedures.
- Examples
- Uses passengers, city populations, customers, demand quantity, and transaction item counts as integer-valued examples.
- What you can do after
- Evaluate whether a measured field represents an integer-valued count or a continuous quantity.
Statistics Every Programmer Needs
Discrete data are observations whose possible values are separate, countable outcomes rather than values throughout a continuous interval.
- How it is used
- Models countable uncertainty in Monte Carlo scenarios such as employee absenteeism and staffing.
- How deep it goes
- Defines countable outcomes and applies them in simulation, but stops short of formal probability derivations.
- Examples
- Uses employee absenteeism and call-center staffing as concrete examples of countable outcomes.
- What you can do after
- Apply it via Monte Carlo simulations to model absenteeism and compare staffing scenarios under uncertainty.
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Related concepts
- continuous datacontrasts with · Continuous data consists of numerical measurements that can take arbitrarily fine-grained values across a range or interval.
- categorical dataequivalent to · 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.
- bar chartused by · A bar chart is a visualization that compares quantitative values for discrete categories using rectangular bars whose lengths or heights encode magnitude.
- integerincludes · An integer is a whole number with no fractional part, including negative numbers, zero, and positive numbers.
- random variablekind of · A random variable is a function that assigns numerical values to outcomes of a random process and is characterized by a probability distribution.
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