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What is a numerical variable and what is a categorical variable?

Aniket Singh , 4 Months ago
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Askiitians Tutor Team

Last Activity: 4 Months ago

A numerical variable is a variable that represents a measurable quantity and takes numerical values that can be meaningfully used in arithmetic operations like addition, subtraction, or averaging. Numerical variables are often classified into two types:

Discrete numerical variables: These take specific, countable values, often whole numbers. For example, the number of students in a classroom (10, 25, etc.) is a discrete variable because it is countable and cannot take fractional values.

Continuous numerical variables: These can take any value within a range, including fractions and decimals. For example, the height of a person (e.g., 5.7 feet) or the time taken to run a race (e.g., 12.3 seconds) are continuous because they can be measured to any desired level of precision.

A categorical variable, on the other hand, represents characteristics or categories and describes qualities or groups rather than quantities. Categorical variables are often divided into:

Nominal categorical variables: These have categories that do not have a meaningful order or ranking. For example, colors (red, green, blue) or types of fruits (apple, banana, orange) are nominal because their categories cannot be ranked.

Ordinal categorical variables: These have categories that can be ranked or ordered, but the differences between the ranks are not necessarily uniform. For example, a satisfaction survey with responses like "very dissatisfied," "dissatisfied," "neutral," "satisfied," and "very satisfied" is ordinal because the responses follow a meaningful order.

Key Differences
Numerical variables: Involve numbers, and arithmetic operations are meaningful.
Examples: Age, temperature, salary.
Categorical variables: Involve categories or groups, and arithmetic operations are not meaningful.
Examples: Gender, eye color, blood type.
Understanding the difference between these variable types is crucial for data analysis, as it helps determine which statistical methods and visualizations to use.

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