We had discussed about the Random Variables previously with the help of examples “heights of people” (**H _{person}**) and “outcome of a dice-roll”(

**O**). In both the cases the kind of values each of the random variables could take were different. The random variable

_{dice-roll}**H**can take any real-value between 0 feet and 15 feet while

_{person}**O**can only take one of the values from set {1,2,3,4,5,6}

_{dice-roll}This tells us that there are different types of data-sets and the value they contain depends on the random-variables they represent. Let us look at the classification around the types of data.

There are majorly two types of data:

- Qualitative Data
- Quantitative Data

### Qualitative Data:

This type of data contains non-numerical values. The values taken by qualitative random variables are categorical in nature. Two broad classes or Qualitative Data:

1. Nominal Data: The values represent categories which are not in any order.

Example: Color of the flower: Red, blue, green

2. Ordinal Data: The values represent categories which follow a particular order.

Example: Income Class: Higher Income, Middle Income, Lower Income

### Quantitative Data:

The Random Variable takes numerical values. Two types of numerical values:

1. Continuous: The variable can take any possible numerical value with in given bouds

Example: Height of a person: 5.5456 feet, 6 feet, 4.00001 feet

2. Discrete: The variable can take only some values with in given bounds

Example: Number of students in a class. The value can only be an integer.

In the upcoming posts we will look at how statistical tools can be applied to each of these types of Random Variables. Thank you!