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In statistics, there are two types of variables:

**1.** **Quantitative Variables: **Sometimes referred to as “numeric” variables, these are variables that represent a measurable quantity. Examples include:

- Number of students in a class
- Number of square feet in a house
- Population size of a city
- Age of an individual
- Height of an individual

**2. Qualitative Variables:** Sometimes referred to as “categorical” variables, these are variables that take on names or labels and can fit into categories. Examples include:

- Eye color (e.g. “blue”, “green”, “brown”)
- Gender (e.g. “male”, “female”)
- Breed of dog (e.g. “lab”, “bulldog”, “poodle”)
- Level of education (e.g. “high school”, “Associate’s degree”, “Bachelor’s degree”)
- Marital status (e.g. “married”, “single”, “divorced”)

Every single variable you will ever encounter in statistics can be classified as either quantitative or qualitative.

**Example: Classifying Quantitative & Qualitative Variables**

Consider the following dataset with information about 10 different basketball players:

There are five total variables in this dataset. Two of them are qualitative variables and three of them are quantitative variables:

**Summarizing Quantitative & Qualitative Variables**

We can use many different metrics to summarize **quantitative variables**, including:

- Measures of central tendency like the mean, median, and mode.
- Measures of dispersion like the range, interquartile range, and standard deviation.

However, we can only use frequency tables and relative frequency tables to summarize **qualitative variables**.

To illustrate this, let’s once again consider the dataset from the previous example:

For the quantiative variable * Seasons Played*, we can calculate the following metrics:

**Mean:**11.5**Median:**12**Mode:**12**Range:**8**Interquartile Range:**4.5**Standard Deviation:**2.915

These metrics give us a good idea of where the center value is located as well as how spread out the values are for this variable.

And for the qualitative variable ** Position**, we can create a frequency table to describe how often different values occur:

This table lets us quickly see how frequently each position (G=guard, F=forward, C=center) occurred in the dataset.

**Additional Resources**

Descriptive vs. Inferential Statistics

Statistic vs. Parameter

Levels of Measurement: Nominal, Ordinal, Interval and Ratio