Ordered variables are those variables that follow a natural rank of order. Some examples of categorical ordered variables from the Bank Marketing dataset are:

- Age group: <30, 30-40, 40-50 and so on.

- Month: Jan, Feb, Mar, etc.

- Education: primary, secondary and so on.

There are other ordered variables in the Bank Marketing data set as well. Let’s perform order univariate analysis on that dataset. At the very beginning of this video, you will see the job variable bar graph, which you have already covered in the previous segments.

Let’s summarise the major takeaways from the above video:

- You have seen that
*education*,*poutcome*and*response*are the ordered categorical variables.

- The bank has primarily contacted those customers who have completed their secondary education. You can observe that in the pie chart below:

- For the majority of the customers, the previous campaign has not been conducted. Refer to the bar graph below to understand more about the ‘
*poutcome’*variable. As you can see, ‘unknown’ has the major share within the ‘*poutcome*‘ variable.

Transition of a numerical variable into an ordered categorical variable.

Let’s consider a very interesting example of your school life. Suppose you have a dataset containing the marks of all the students in the ‘Science’ subject, and you are one of the students in that group. These marks can be considered as categorical if you divide the total marks into different categories like High, Medium, Average, Below Average, Poor. From this analysis, you can determine your ranking in the class and also find out how many students got more marks than you and how far away your score is from the **mean **or the** average **score.

The important thing to note here is that your marks are a numerical variable, which you have then categorised into ‘high marks’ and ‘low marks’. This is an approach that you will need to adopt in the future, and you will learn more about this approach in the next segment on numerical variable analysis.

In the next video, you will understand the basics of statistics and its applications in real-life examples.

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