Variable Statistical Types
| Type | Description | Example |
|---|---|---|
| Continuous |
Values are numerical, continuous, and sortable. They can be used to calculate measures; for example, mean or variance. During modeling, a continuous variable may be grouped into significant discrete bins. |
The variable <salary> is both a numerical variable, and a continuous variable. It may, for example, take on the following values: <$1,050>, <$1,700,> or <$1,750>. The mean of these values may be calculated. |
| Ordinal |
Values are discrete. They can be regrouped into categories and are sortable. Ordinal variables may be:
|
The variable <school grade> is an ordinal
variable. Its values actually belong to definite categories and
can be sorted. This variable can be:
|
| Nominal |
Values are discrete. They can be regrouped into categories. Caution Binary variables (variable with 2 distinct values only) are considered
as nominal variables. They are the ones that can be used as
target for classification predictive models |
The variable <zip code> is a nominal variable. The set of values that this variable may assume are clearly distinct, non-ranked categories, although they happen to be represented by numbers. For example: <10111>, <20500> or <90210>. The variable <eye color> is a nominal variable. The set of values that this variable may assume are clearly distinct, non-ordered categories, and are represented by character strings. For example: <blue>, <brown>, <black>. |
| Textual Note These variables are currently not supported by
Smart Predict, and are therefore excluded
from the training of a predictive model. |
A type of nominal variable containing phrases, sentences, or complete texts. Textual variables are used for text analyses. | For example the variable <Bluetooth Headphones Customer Feedback> is a textual variable. The values for this variable can be <Durable cord, connect easy to phone and plug.>, <Great fit and great sound!> or <Great length and color. Super fast charging.>. |