1: Beyond the scatterplot

A few modules ago, we reviewed scatterplots. And we made lots of scatterplots (yeah, I got to
make them too, remember?). Lots and lots of scatterplots. In fact, every single plot we made
was a scatterplot. Does this get a little boring?

In this module, we’ll spice things up with some other kinds of graphs. Below are two graphs
relating to fish food.

scatterplot: growth vs. amount of Fish2Whale barchart: growth vs. type of food


The one on the left is obviously a scatterplot. The one on the right is a bar graph. But how do you know that you need to use a bargraph for your data? Here are some data tables associated with these two graphs:

scatterplot vs. barchart
Amount of Fish2Whale (g/day) 2 3 4 5 6 8 10
growth (grams/day) 0.1 0.12 0.14 0.14 0.17 0.17 0.17
Type of fishfood Budget Fude Ball-
growth (grams/day) 0.05 0.06 0.07 0.15


Note that these tables do not lead directly to the graphs – there is some computation that converts the numbers in the tables to the data that gets plotted in the graphs so don’t be too concerned if don’t see the direct connection.  What is important is the type of data represented in the tables. The question that needs to be asked is: What is the essential difference between these two data tables? If you said, the first data table is all numbers, but the second data table has some information that is not in the form of numbers, then you are correct.

The use of the scatter plot in the first graph reflects the fact that in the first table the amount of food given to the fish per day is given as a numeric quantity.  We use the word “quantitative” means to describe data that can be quantified or counted. In the first table the amount of food given per day is given using numbers. In other words, quantitative data can be represented by a number.

In contrast the the second graph uses bars because the type of fish food is not a numeric quantity.  It is known as nominal data – so the variable that describes type of fish food is given using a name (a word).  This is also known as “qualitative data.” In this case, “qualitative” means data that you can qualify, or describe, but not count.

Quantitative data can be put on a quantitative axis which allows comparison between values on that axis. For example on the scatter plot we know that fish being fed 10 mg/day are being given twice as much as fish being given 5mg/day. Qualitative data can’t be interpreted in that way. For example the bar chart reflects the fact that I have four categories in the data table. There is no relationship between the fish food types and the best I can do is simply list each category on x-axis.