What Are Variables in Statistics?

In statistics, a variable has two defining characteristics:

• A variable is an attribute that describes a person, place, thing, or idea.
• The value of the variable can "vary" from one entity to another.

For example, a person's hair color is a potential variable, which could have the value of "blond" for one person and "brunette" for another.

Qualitative vs. Quantitative Variables

Variables can be classified as qualitative (aka, categorical) or quantitative (aka, numeric).

• Qualitative. The value of a qualitative variable is a name or a label. The color of a ball (e.g., red, green, blue) or the breed of a dog (e.g., collie, shepherd, terrier) would be examples of qualitative or categorical variables.
• Quantitative. The value of a quantitative variable is a number. For example, when we speak of the population of a city, we are talking about the number of people in the city - a numerical attribute of the city. Therefore, population would be a quantitative variable.

In algebraic equations, quantitative variables are represented by symbols (e.g., x, y, or z).

Discrete vs. Continuous Variables

Quantitative variables can be further classified as discrete or continuous. If a variable can take on any value between its minimum value and its maximum value, it is called a continuous variable; otherwise, it is called a discrete variable.

Some examples will clarify the difference between discrete and continouous variables.

• Suppose the fire department mandates that all fire fighters must weigh between 150 and 250 pounds. The weight of a fire fighter would be an example of a continuous variable; since a fire fighter's weight could take on any value between 150 and 250 pounds.
• Suppose we flip a coin and count the number of heads. The number of heads could be any integer value between 0 and plus infinity. However, it could not be any number between 0 and plus infinity. We could not, for example, get 2.3 heads. Therefore, the number of heads must be a discrete variable.

Univariate vs. Bivariate Data

Statistical data are often classified according to the number of variables being studied.

• Univariate data. When we conduct a study that looks at only one variable, we say that we are working with univariate data. Suppose, for example, that we conducted a survey to estimate the average weight of high school students. Since we are only working with one variable (weight), we would be working with univariate data.
• Bivariate data. When we conduct a study that examines the relationship between two variables, we are working with bivariate data. Suppose we conducted a study to see if there were a relationship between the height and weight of high school students. Since we are working with two variables (height and weight), we would be working with bivariate data.

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