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# Interval Data

The first type of numerical data that we encounter
in data science are interval data.
Interval data are a type of numerical data.
That is, they represent measured quantities
of things.
Interval data allow for a degree of difference
between two values
(i.e. we can add or subtract the values in
meaningful ways).
However, interval scales have an arbitrary
zero point on their scale
(i.e. the place were zero appears on the scale
was chosen for convenience not because it
represents a true absence of the thing being
measured.
So there is no concept of a ratio between
two numbers or the ability to multiply or
divide two numbers in any meaningful way.
For example, imagine a thermometer measuring
outdoor temperature.
The zero point on a Celsius thermometer represents
the temperature where water freezes.
This is simply for convenience zero on this
scale does not represent absolute zero heat,
like it does on the Kelvin scale.
The difference between 20°C and 30°C (which
is a 10° change) is the same difference in
temperature as a change from 40° to 50°
(also a 10° change).
So we can perform addition and subtraction
with this interval scale.
However, it doesn’t make sense to say that
20°C is half as hot as 40°C or that 40°C
is twice as hot as 20°C.
This is because 0°C isn’t the absence of
all heat but rather was an arbitrarily chosen
point on the scale where water freezes.
So it simply doesn’t make sense to discuss
ratios, multiplication, or division with the
Celsius temperature scale or other interval
scales.
Other examples of interval data include:
IQ scores,
dates on a calendar,
and longitudes on a map.
The key distinction is that the zero point
on an interval scale is arbitrarily chosen;
it doesn’t represent a natural minimum quantity
of the thing being measured.
We can perform a few more mathematical operations
on interval data than we can on nominal and
ordinal data.
In addition to testing for equality, sorting
by order, and determining both the mode and
the median.
We can also add or subtract interval data.
In addition, we can also determine the arithmetic
mean (i.e. the average value in a set of interval values).
Interval data are a bit more powerful than
nominal and ordinal data in terms of mathematical
operations, but still not as powerful as ratio
data.