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Meaning, Advantages and Difference Between Partial Correlation and Multiple Correlation. Explain with Examples
Partial Correlation Coefficient
Partial Correlation studies the linear relationship between two variables after excluding the effect of one or more other variables. Partial correlation co-efficient exists between given two variables after eliminating the variance from a third variable. The relationship between two variables is examined after the third variable is eliminated from both. The partial correlation co-efficient varies between -1 and +1.
Advantages of Partial Correlation Coefficient
i. Measures the strength of a relationship between two variables while controlling for the effect of one or more variable.
ii. Partial correlation makes it possible to remove the influence of a third variable from the relationship.
iii. It allows to see correlations between each predictor and the response.
Multiple Correlation Coefficient
Multiple correlation coefficient is used to evaluate the maximum degree of liner relationship that can be obtained between two or more independent variables and a single dependent variable. It is used to measure how well a given variable can be predicted using a linear function of a set of other variables. The multiple correlation coefficient value varies between 0 to 1.
Advantages of Multiple Correlation Coefficient
i. Multiple correlation measures one variable with another variable and gives a better result.
ii. Multiple correlation provides prediction about a variable as it is based on three or more variables.
Difference Between Partial Correlation and Multiple Correlation Coefficient
i. Partial correlation coefficient demonstrates a linear relationship between two variables
after removing the effect of one or more independent variables. In multiple correlation coefficient three variables are studied simultaneously.
ii. The partial correlation co-efficient varies between -1 and +1 whereas the multiple correlation coefficient value lies between zero and one.