Mutually inclusive classification is usually meant for a discrete variable. Let's understand this concept in detail.

Discrete variable
A discrete variable is a type of variable that takes on a finite or countable number of values. For example, the number of students in a class is a discrete variable because it can only take on whole number values such as 1, 2, 3, 4, and so on.

Mutually inclusive classification
Mutually inclusive classification is a method of grouping data into categories where an item can belong to more than one category. It means that the categories are not mutually exclusive. For example, a student in a class can be classified into multiple categories such as male, over 6 feet tall, and plays basketball.

Importance of mutually inclusive classification for a discrete variable
Mutually inclusive classification is particularly useful for discrete variables because it allows us to capture all possible combinations of values that an item can take. For example, if we have a discrete variable that represents the color of a product, we can use mutually inclusive classification to group the products into categories based on their color combinations. This can be useful in marketing campaigns where we want to target customers who prefer certain color combinations.

In conclusion, mutually inclusive classification is usually meant for a discrete variable because it allows us to capture all possible combinations of values that an item can take. By using this method, we can group data into categories that are not mutually exclusive and capture more nuanced information about the data.

Discrete variables are those which can be counted in whole nos.
mutually inclusive classification means like 40-49,50-59,etc...directly whole nos.