![]() ![]() ![]() A curvi-linear, or a non-linear correlation. On the other hand, when the data of two variables plotted on a graph paper give out a curve of any direction, it is a case of curvi-linear, or a non-linear correlation. The linear correlation, again, may be either positive, or negative in nature, and accordingly, it may give either an upward, or a downward straight line when plotted on a graph paper. The linear constancy in the ratio of changes between the values of the variables. This is possible only when there is perfect relationship, or constancy in the ration of changes between the values of the variables. When the data relating to correlation plotted on a graph paper give rise to a straight line, it is a case of linear correlation. It is to be noted that the perfect, and imperfect correlations speak of the degree of correlation which is ascertained by computing the co-efficient. Similarly, imperfect correlation can either be of imperfect negative nature. Thus, perfect correlation can either be of perfect positive, or perfect negative nature. When correlations are measured mathematically, the value of perfect correlation will be either + 1 or -1 ,and the value of imperfect correlation will be between ± 1. On the other hand, when the values of the variables under study change at different ratios, it is a case of imperfect correlation. When the values of both the variables under study change at a constant ratio irrespective of the direction, it is a case of perfect correlation. Sales profitsġ.Example of positively correlated data (iii) Perfect and imperfect correlation. The following data illustrate the examples of positive, and negative correlation. It is to be noted that the data of positive correlation when plotted on a graph paper will give an upward curve whereas the data of negative correlation, if plotted on a graph paper will give a downward curve. ![]() and increase in the value of one is followed by a decrease in the value of the other, and a decrease in the value of one is followed by a decrease in the value of the other, and a decrease in the value of one is followed by an increase in the value of the other. On the other hand, when both the variables under study move in the opposite direction, i.e. with an increase in the value of one variable, the value of the other variable increases, and with a decrease in there value of one variables, the value of the other variable decreases, it is a case of positive correlation. When the value of both the variables under study move in the same direction, i.e. In actual practice, however, the study of multiple correlation is not popular. For example, if we study the relationship between the volume of profits, volume of sales, and the volume of cost of sales at a time, it will be a case of multiple correlation. On the other hand, when the relationship between any two, or more variables is studied at a time, it is a case of multiple correlation. marks in English, it will be a case of partial correlation. marks in statistics, and marks in Accountancy ignoring the effect of the other variables i.e. When the relationship between any two out of three, or more variables is studied For example, if out of the three related variables, say, marks in Statistics, Marks in Accontancy, and marks in English, we study the correlation between the two variables viz. When the relationship between any two variables only is studied, it is a case of simple correlation. (i) Simple, Partial, and multiple Correlation. (iii) Perfect, and Imperfect correlation. (i) Simple, Partial, and Multiple correlation. There are different types of correlation which may be noted between any two, or more variables, These different types may be ramified into the following classes: ![]()
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