In a bivariate distribution we may be interested to find out if there is any correlation or covariation between the two variables under study. If the changes in one variable affects a change in the other variable, the variables are said to be correlated. If the two variables deviate in the same direction, that is if the increase in one results in a corresponding increase in the other, correlation is said to be direct or positive. But if they constantly deviate in the opposite direction, that is if increase in one results in corresponding decrease in the other, correlation is said to be diverse or negative.
It is simplest way of the diagrammatic representation of bivariate data. Thus for the bivariate distribution (xi,yi) ; i=1,2,………n. if the values of the variables X and Y be plotted along the x-axis and y-axis respectively in the xy plan, the diagram of dots so obtained is known as scatter diagram. From the scatter diagram, we can form a fairly good, though vague, idea whether the variables are correlated or not, e.g., if the points are very dense, i.e., very close to each other we should expect a fairly good amount of correlation between the variables and if the points are widely scattered, a poor correlation is expected. This method, however, is not suitable if the number of observations is fairly large.
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