DIFFERENTIATE BETWEEN SIMPLE, MULTIPLE AND PARTIAL CORRELATION.
The distinction between simple, partial and multiple correlation is based upon the number of variables studied. When only two variables are studied it is a problem of simple correlation. When three or more variables are studied it is a problem of either multiple or partial correlation. In multiple correlation three or more variables are studied simultaneously. For example, when one studies the relationship between the yield of rice per acre and both the amount of rainfall and the amount of fertilizers used, it is a problem of multiple correlation. On the other hand, in partial correlation we recognize more than two variables, but consider only two variables to be influencing each other, the effect of other influencing variables being kept constant. For example, in the price problem taken above if we limit our correlation analysis of yield and rainfall to periods when a certain average daily temperature existed, it becomes a problem relating to partial correlation only.
Partial correlation is a process in which we measure of the strength and also direction of a linear relationship between two continuous variables while controlling for the effect of one or more other continuous variables it is called ‘covariates’ and also ‘control’ variables.in partial correlation between independent and dependent variables has not distinction.
Multiple correlation is the process in statistics, the coefficient of multiple correlation is the measure of how well a given variable can be predicted using a linear function of the set other variables. It is a correlation between the different variable’s values and the best predictions that can be computed linearly from the predictive variables. The coefficient of multiple correlation takes the values between 0 and 1.