In statistics, a 'variable' refers to a characteristic of data that is measurable or observable. A variable could be something like temperature, mass, height, make of car, type of animal or goals scored. We often collect data to observe and analyse changes in a variable.
Data variables can be defined as either numerical or categorical.
Discrete numerical data involve data points that are distinct and separate from each other. There is a definite 'gap' separating one data point from the next. Discrete data usually, but not always, consists of whole numbers, and is often collected by some form of counting.
Examples of discrete data:
Number of goals scored per match | $1$1, $3$3, $0$0, $1$1, $2$2, $0$0, $2$2, $4$4, $2$2, $0$0, $1$1, $1$1, $2$2, ... |
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Number of children per family | $2$2, $3$3, $1$1, $0$0, $1$1, $4$4, $2$2, $2$2, $0$0, $1$1, $1$1, $5$5, $3$3, ... |
Number of products sold each day | $437$437, $410$410, $386$386, $411$411, $401$401, $397$397, $422$422, ... |
In each of these cases, there are no in-between values. We cannot have $2.5$2.5 goals or $1.2$1.2 people, for example.
This doesn't mean that discrete data always consists of whole numbers. Shoe sizes, an example of discrete data, are often separated by half-sizes. For example, $8$8, $8.5$8.5, $9$9, $9.5$9.5. Even still, there is a definite gap between the sizes. A shoe won't ever come in size $8.145$8.145.
Continuous numerical data involves data points that can occur anywhere along a continuum. Any value is possible within a range of values. Continuous data often involves the use of decimal numbers, and is often collected using some form of measurement.
Examples of continuous data:
Height of trees in a forest (in metres) | $12.359$12.359, $14.022$14.022, $14.951$14.951, $18.276$18.276, $11.032$11.032, ... |
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Times taken to run a $10$10 km race (minutes) | $55.34$55.34, $58.03$58.03, $57.25$57.25, $61.49$61.49, $66.11$66.11, $59.87$59.87, ... |
Daily temperature (degrees C) | $24.4$24.4, $23.0$23.0, $22.5$22.5, $21.6$21.6, $20.7$20.7, $20.2$20.2, $19.7$19.7... |
In practice, continuous data will always be subject to the accuracy of the measuring device being used, so is generally rounded. However, given a height measured to the nearest centimetre of $165\ cm$165 cm we know that the height lies on the interval $\left[164.5,165.5\right)$[164.5,165.5). So unlike discrete numbers, such measurements are on a continuous interval with no gaps between neighbouring measurements.
The word 'ordinal' basically means 'ordered'. Ordinal categorical data involves data points, consisting of words or labels, that can be ordered or ranked in some way.
Examples of ordinal data:
Product rating on a survey | good, satisfactory, good, excellent, excellent, good, good, ... |
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Exam grades | A, C, A, B, B, C, A, B, A, A, C, B, A, B, B, B, C, A, C, ... |
Size of fish in a lake | medium, small, small, medium, small, large, medium, large, ... |
Ordinal data is often used in surveys such as a service rating (poor, average, good, excellent), results can then be further analysed by changing the ordered ratings to numerical data.
The word 'nominal' basically means 'name'. Nominal categorical data consists of words or labels, that name individual data points.
Examples of nominal data:
Nationalities in a sporting team | German, Austrian, Italian, Spanish, Dutch, Italian, ... |
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Make of car driving through an intersection | Toyota, Holden, Mazda, Toyota, Ford, Toyota, Mazda, ... |
Hair colour of students in a class | blonde, red, brown, blonde, black, brown, black, red, ... |
Nominal data is often described as 'un-ordered' because it can't be ordered in a way that is statistically meaningful.
Which two of the following are examples of numerical data?
favourite flavours
maximum temperature
daily temperature
types of horses
Classify this data into its correct category:
Weights of dogs
Categorical Nominal
Categorical Ordinal
Numerical Discrete
Numerical Continuous