Correlation Analysis

Abstract

Every individual element in any population is composed of several quantitative as well as qualitative characters. A poultry breed is being characterized by its size, shape, color, body weight, egg-laying capacity, etc. A variety of paddy is known by its growth, yield, and other characters like plant height, number of tillers per hill, panicle length, grain size and shape, grain weight, resistance to different pest and diseases, stress tolerance, etc. Most of these characters are related with each other; for example, the body weight of poultry bird varies with that of the age and the egg-laying capacity also varies with the type of breed as well as the age of the birds. Similarly, the number tillers per hill and number of effective tiller per hill, panicle length, and number of grains per panicle are associated with each other. In statistics, we study the population characters, and in population, many characters are associated with each other. While studying the population in terms of its characteristics, one may study the characters taking one at a time and can find out different measures of central tendency, dispersion, etc. for individual characters separately. But as we have just discussed, a close look in to the characters will clearly suggest that none of the characters vary in isolation; rather, these have a tendency to vary together. Hence, the importance of studying the characters together are felt. If we study many number of variables at a time, then we call it multivariate study, and when we study two variables at a time, it is known as the bivariate study. Thus, the simplest case in multivariate study is the bivariate study.