The cumulation of these data of any phenomenon and process that we collect is called data. Every observation gives us a data.
- Nominal Data: (Categorical = Qualitative)
It represents more than 2 strict levels or types.
Variables can be age, gender, employment status, married status, colors, etc.
We can represent variables by using words, texts and numerical code without any order.
We can use frequency to show each variable’s percentage however there should not be any mean or average.
Nominal data are represented by using pie chart or bar-column chart.
- Ordinal Data:
It represents rank, satisfaction and fanciness and while representing them the differences between ranks, satisfactions or fanciness may not equal. So, there is a hierarchy between variables.
For instance, in a footrace the distance between 1st and 2nd runners and 2nd and 3rd runners may not be equal.
We can use frequency to show each variable’s percentage however there should not be any mean or average.
Ordinal data are represented by using bar-column chart with the help of logical order.
- Interval/Rational Data: (Scale = Quantitative = Parametric)
It represents the relationship between measured variables in an equative amount of ranks to comparison.
We can represent variables by using discrete and continuous numbers.
We can use frequency to show each variable’s percentage as well as mean, median and standard deviation to be able to show that mathematics is versatile.
Interval/Rational data are represented by using bar-histogram chart.
- In a survey;
Types of chocolates – nominal data
The measure of satisfaction or like hood – ordinal data
Age, amount spend on market and number of taken chocolate of participants – interval/ ratio data
- The height of person X & Y objects A & B
A is tall and B is short – nominal data
X is taller than Y—ordinal data
On a scale where the difference between each group is equal and 20cm, X is which is 190cm taller than Y which is 170cm—Interval data
A is twice as tall as B – ratio data
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