Mutually exclusive classification is usually meant for continuous variable. Let's understand this concept in detail.

Explanation:

- Mutually exclusive classification refers to the process of categorizing data into distinct and non-overlapping groups or classes.
- This means that each data point can belong to only one class and cannot belong to any other class at the same time.
- For example, if we are classifying students based on their grades, a student who scored an A cannot be classified as a B or C student.
- Mutually exclusive classification is commonly used in statistics, data analysis, and machine learning to simplify data and make it easier to analyze.
- It is usually applied to discrete variables, which are variables that can only take on a finite set of values, such as categories or counts.
- However, it can also be applied to continuous variables, which are variables that can take on any value within a certain range or interval, such as height or weight.
- In the case of continuous variables, mutually exclusive classification involves dividing the range of values into non-overlapping intervals or bins.
- Each data point is then assigned to the interval or bin that it falls into based on its value.
- This allows us to group data into meaningful categories and analyze patterns and relationships within each category.

Conclusion:

In conclusion, mutually exclusive classification is usually meant for continuous variables, as well as discrete variables. It involves dividing data into non-overlapping categories or bins based on their values, and is commonly used in statistics, data analysis, and machine learning to simplify and analyze data.

Option B is correct only for continuous series