Once we have gathered data, we might wish to classify it. Roughly speaking, data can be classified as categorical data or quantitative data.

Quantitative and categorical data

**Categorical (qualitative) data** are pieces of information that allow us to classify the objects under investigation into various categories.

**Quantitative data** are responses that are numerical in nature and with which we can perform meaningful arithmetic calculations.

### Example 3

We might conduct a survey to determine the name of the favorite movie that each person in a math class saw in a movie theater.

When we conduct such a survey, the responses would look like: *Finding Nemo*, *The Hulk*, or *Terminator 3: Rise of the Machines*. We might count the number of people who give each answer, but the answers themselves do not have any numerical values: we cannot perform computations with an answer like “*Finding Nemo*.” This would be categorical data.

### Example 4

A survey could ask the number of movies you have seen in a movie theater in the past 12 months (0, 1, 2, 3, 4, …)

This would be quantitative data.

Other examples of quantitative data would be the running time of the movie you saw most recently (104 minutes, 137 minutes, 104 minutes, …) or the amount of money you paid for a movie ticket the last time you went to a movie theater ($5.50, $7.75, $9, …).

Sometimes, determining whether or not data is categorical or quantitative can be a bit trickier.

### Example 5

Suppose we gather respondents’ ZIP codes in a survey to track their geographical location.

ZIP codes are numbers, but we can’t do any meaningful mathematical calculations with them (it doesn’t make sense to say that 98036 is “twice” 49018 — that’s like saying that Lynnwood, WA is “twice” Battle Creek, MI, which doesn’t make sense at all), so ZIP codes are really categorical data.

### Example 6

A survey about the movie you most recently attended includes the question “How would you rate the movie you just saw?” with these possible answers:

1 – it was awful

2 – it was just OK

3 – I liked it

4 – it was great

5 – best movie ever!

Again, there are numbers associated with the responses, but we can’t really do any calculations with them: a movie that rates a 4 is not necessarily twice as good as a movie that rates a 2, whatever that means; if two people see the movie and one of them thinks it stinks and the other thinks it’s the best ever it doesn’t necessarily make sense to say that “on average they liked it.”

As we study movie-going habits and preferences, we shouldn’t forget to specify the population under consideration. If we survey 3-7 year-olds the runaway favorite might be *Finding Nemo*. 13-17 year-olds might prefer *Terminator 3*. And 33-37 year-olds might prefer…well, *Finding Nemo*.

### Try it Now 3

Classify each measurement as categorical or quantitative

a. Eye color of a group of people

b. Daily high temperature of a city over several weeks

c. Annual income