Screen Link: https://app.dataquest.io/m/284/variables-in-statistics/4/the-nominal-scale
Context: Nominal variables are variables like names & categorical data that cannot be compared like quantitative variables. After explaining this, a task is given - Add the variables measured on a nominal scale to a list named
nominal_scale , and sort the elements in the list alphabetically
It seems to be unclear as to whether we have to append all the variables into a list or whether only the names of the variables (or column names) need to be appended and sorted. Does anyone else feel that way? Variables can also mean the data. Right?
It is slightly unclear.
They do seem to make a little distinction based on how they phrase the content around this -
In the previous screen, we’ve discussed about the
Team variable, and said that by examining its values we can tell whether two individuals are different or not,
The use of
variable seems interchangeable with the name of the
variable. And for the data within the
variable they refer to the values in it.
In fact, in previous Steps they use the name of the
variable as a label to describe the values stored in it.
Each row describes an individual having a series of properties: name, team, position on the field, height, etc. For most properties, the values vary from row to row. All players have a height, for example, but the height values vary from player to player.
The properties with varying values we call variables . The height property in our data set is an example of a variable. In fact, all the properties described in our data set are variables.
I think it is reasonable to assume that
variable refers to that name/label instead of the value stored in the corresponding column.