Quantitative Classification

The Quantitative classification refers to the classification of data according to some characteristics that can be measured, like weight, height, income, sales, production, profits, etc. for e.g. The students of a college may be classified according to the weight as follows:

Such a distribution is termed as empirical frequency distribution or simple frequency distribution.

In this kind of classification, there are two elements, namely (I) the variable i.e. the weight in the example above, and (ii) the frequency, i.e. the number of students in every class. There were 50 students having weight ranging from 90 to 100 lb., 200 students having the weight ranging from 100 to 110 lb, and so on. Thus we can determine the ways in which the frequencies are distributed.

A frequency distribution refers to the data classified on the basis of some variable that can be measured such as wages, prices, age, number of units produced or consumed. The term variable refers to the characteristic that varies in the amount of magnitude in a frequency distribution. The variable may either be continuous or discrete. A continuous variable also termed continuous random variable is capable of manifesting each conceivable fractional value within the weight of a product. In a continuous variable, hence data are obtained by numerical measurements rather than counting. For e.g., when a student grows, from 90 cm. to 150 cm. his height passes through all values between these limits on the other hand, a discrete variable is that which can vary only by finite jumps and cannot manifest every conceivable fractional value. For illustrate the number of rooms in a house can only take certain values such as 1, 2, 3, etc. Similarly, the no. of machines in an establishment are discrete variables. Normally speaking, the Continuous data are obtained through measurements, while the discontinuous data are derived by counting. Serves which can be represented by a discrete variable are termed as discrete series. The following are the two examples of continuous and discrete frequency distributions.

 Although the theoretical difference between the continuous and discrete variations is clear & precise. The practical statistical work is only an approximation. The reason is that even the most precise instruments of measurement can be used only to a finite number of times. Thus every theoretically continuous series can never be expected to flow continuously with one measurement touching another without any break in the actual observations.