# One Attribute

Suppose that there are **100** individuals in a certain sample, and the sample size is denoted by . These **100** individuals are divided into two mutually exclusive groups on the basis of the attribute of height. Out of **100, 60** are tall and **40** are short. If tall is denoted by and short is denoted by , we can write:

There are two groups and thus we say that there are two classes, and , and the class frequency under is **60**. This is written as ; similarly the number of individuals under is written as . Thus the attributes written within the brackets show their class frequencies. In this example the sample is divided into two groups, i.e.; two classes, tall and short. Dividing the data into two groups is called a dichotomy, which means cutting into two. In this example a single attribute ‘height’ divides the data in two groups. As only one attribute is involved, the data is called one-way classification. We can make a small table as below:

Clearly