In some scientific works, once the data have been gathered from a population of interest, it is often difficult to get a sense of what the data indicate when they are presented in an unorganized fashion. Assembling the raw data into a meaningful form, such as a frequency distribution, makes the data easier to understand and interpret. It is in the context of frequency distributions that the importance of conveying in a succinct way numerical information contained in the data is encountered.
So, grouped data is data that has been organized into groups known as classes. The raw dataset can be organized by constructing a table showing the frequency distribution of the variable (whose values are given in the raw dataset). Such a frequency table is often referred to as grouped data.
Here, we developed a m-code to calculate the range of a grouped data. One can input the returns or modified vectors n and xout containing the bin locations of the hist m-function, in a data vector.

Grouped range calculation uses the formula of class boundaries,

R = Uc - L1

where:
L1 = lower boundary of class 1
Uc = upper boundary of class c (last class)

Syntax: function y = grange(x)

Input:
x - class mark data

Output:
y - range of the values in x