Final answer:
Attributes such as eye_color, weight, student_id, and student are examples of qualitative data, which describe or categorize features of a dataset and cannot be expressed numerically with an inherent arithmetic meaning.
Step-by-step explanation:
The statement suggests that eye_color, weight, student_id, and student are examples of attributes. In the context of data, attributes refer to characteristics or qualities that describe entities within a dataset. When we are dealing with data, attributes are indeed individual pieces of information that define specific aspects of a dataset. Attributes such as eye color or student ID are examples of qualitative data, representing categories rather than numerical values.
Qualitative data are non-numeric and used to categorize or describe attributes of a population. Examples include hair color, ethnic group, and the type of car a person drives. Unlike quantitative data, which can be expressed as numbers and used for calculations, qualitative data describe characteristics that do not lend themselves to mathematical analysis. For instance, it would not be meaningful to calculate the average hair color or blood type.
In a database context, an attribute like student_id might be used as a unique identifier for a student record. Other attributes like eye_color could be used to store information about the physical characteristics of the person associated with that record. It is important to note that qualitative attributes provide a rich source of information for classification, sorting, and identifying patterns within data.