# Types of Attributes Knowledge about the types of the attributes can provide guidance in selecting graphical encoding strategies. For any type of dataset, there are 3 types of attributes: * **Categorical**: two or more categories but no intrinsic ordering, ex. hair color: blonde, brunette, black * **Ordinal**: two or more categories with intrinsic ordering, but distance between values is unknown (therefore, no arithmetic operations can be performed on the values), ex. economic status: low, medium, high * **Quantitative**: the values represent a **measured** quantity, and the distance between them is known/can be computed (therefore, arithmetic operations can be performed on the values), ex. height, weight **Note**: Just because the values are numeric, it doesn't mean that the attribute is quantitative. For example, 'order id' will have numeric values, but it is ordinal (or even categorical, if the ordering is unknown), but not quantitative, because the values do not represent a measured quantity. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://vikram-bajaj.gitbook.io/cs-gy-6313-information-visualization/data-abstraction-and-transformation/types-of-attributes.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.