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.