Continuous data consists of numerical measurements that can take values across a range, including arbitrarily fine subdivisions. In tabular analysis, it contrasts with categorical data, which belongs to a discrete set. section 6.1
Classify continuous measurements by their scale:
- Interval data has evenly spaced values, but zero does not represent an absence of the measurement; temperature is an example. section 6.1
- Ratio data has evenly spaced values and a meaningful zero representing absence; height and weight are examples. section 6.1
- Numeric-looking columns are not automatically continuous: sex is treated as nominal categorical data rather than averaged like age. section 6.1
Patient measurements
A heart disease dataset combines continuous measurements such as age with categorical fields such as sex. Identifying these types determines which summaries and analyses are appropriate. section 6.1
Table 6.1 An overview of the different types of data
|
Name
|
Categorical or continuous?
|
Properties
|
Example
|
|---|---|---|---|
|
Nominal
|
Categorical
|
Data has discrete values, and categories are not ordered.
|
Colors, political parties
|
|
Ordinal
|
Categorical
|
Data has discrete values, and categories have a natural order, but spacing is uneven between categories.
|
T-shirt sizes (S/M/L), survey responses (good versus bad)
|
|
Interval
|
Continuous
|
Values are spaced evenly, but zero is not an absence of the measurement.
|
Temperature
|
|
Ratio
|
Continuous
|
Values are spaced evenly, and zero means an absence of the measurement.
|
Height, weight
|
Use the scale overview to compare nominal, ordinal, interval, and ratio data by discreteness, ordering, spacing, and zero meaning. section 6.1
