There are two main types of data that we might encounter, categorical and quantitative.

*For example, how many ounces of olive oil are in each American home. If three families told you how many ounces of olive oil they have, you could put them in a meaningful order- from least to greatest, or greatest to least.*

**Quantitative data**are quantities, numbers that have both order and consistent spacing.This order also has consistent spacing. An increase in one ounce of olive oil is the same, whether you go from zero to one ounce, or from a hundred to a hundred and one ounces. These properties allow us to do simple math with the data, like taking the mean, or calculating the standard deviation.

*For example, favorite kind of pasta. You might like penne, rotini, linguine, even angel hair. But there's no objective way to put those pastas into a meaningful order. Is penne truly better than linguine? Where does rotini fit in? It would be pasta madness to try to put them in order!*

**Categorical data**doesn't have a meaningful order or consistent spacing.The simplest way to display categorical data is to make a frequency table. A

**frequency table**shows you all the categories and all the numbers of data points that fall in that category. In other words, it's frequency. To change a frequency table into a

**relative frequency table**, we just need to take each raw frequency and divide by the number of

*total*points to get a decimal between zero and one. Some of you may be used to reading decimals as percentages, but if you're not, just multiply by a hundred to get the percentage. For linguine, we have ten divided by fifty, which is point two, or twenty percent of the group.

Relative frequency tables have the benefit of being easy to compare. No matter what we're measuring or how many data points we have, it's easy to compare percentages. If twenty percent of people like linguine, we can see that's a smaller percent than the sixty-seven percent of people who like pineapple on pizza, or greater than the ten percent of my family who thinks statistics are scary. The relative frequency table for a favorite pasta might look like this.

We can also add more than one variable to our frequency table. We could ask people to rate their favorite pasta sauce, and make a combined frequency table, or a contingency table of both pasta and sauce preference. If I were planning a party, and needed to pick some pasta for the group, my best bets would be the rotini with red sauce, and penne with red or white sauce. And because I'm planning a party, and because I'm having food, I did look it up- the chance of death by choking on food in the U.S in a given year is one in one-hundred thousand six-hundred eighty-six.

But sometimes, we don't want just numbers in our visualization. Earlier in the series, I talked about how it can be hard to wrap your head around numbers, especially when they get really big or really small. There are other more visual ways to represent categorical data.