The second type of numerical data that we
encounter in data science are ratio data.
Ratio data are a type of numerical data.
That is, they represent measured quantities
Ratio data allow for a degree of difference
between two values, just like interval data.
However, unlike interval data, ratio scales
do have a natural (non-arbitrarily chosen)
So the concept of a ratio, and multiplying
or dividing two values make perfect sense.
For example, imagine we have two apples:
One has a mass of 100 grams and the other
has a mass of 200 grams
Unlike an interval scale, it makes perfect
sense to say that a 100-gram apple is half
the mass of a 200-gram apple.
This is because zero grams on this scale represents
a natural minimum quantity (i.e. no mass at all).
So 200 grams of mass is twice as much mass
as 100 grams of mass.
Other examples of ratio data include:
the distance between two points,
income from your job,
and elapsed time.
The key distinction (once again) between interval
and ratio scales is that the zero point on
a ratio scale represents a natural zero quantity
of the thing being measured.
It can be difficult to recognize the subtle
yet important difference between interval
scales and ratio scales.
So if you’re having difficulty understanding,
you may want to research this topic further.
We can perform a few more mathematical operations
on ratio data than we can on nominal, ordinal,
and interval data.
In addition to all of the operations we’ve
seen so far, we can also multiply and divide
In addition, we can determine the geometric
mean, which is a method of averaging used
for values with widely varying ranges.
Ratio data are the most powerful type of data
we encounter in data science in terms of mathematical operations.