The partial correlation analysis explained as above has a number of utilities which may be briefed as under:

(a) It enables us to study the effect of one independent variable on a dependent variable where there are other independent variables having the effects on the same dependent variable.

(b) It provides us with a technique by which we can measure the relationship between any two variables keeping the other influential variables as constants or ineffective.

(c) It enables us to make a meaningful correlation analysis in the field of social sciences like, economics, commerce etc., where most of the phenomena have multiple causation, and a number of variables are in operation at the same time, whose effects cannot be studied separately as it happens in physical and experimental sciences.

(d) It can be used in conjunction with the simple and multiple correlation in the analysis of factors affecting variations in many kinds of phenomena.

(e) It expresses the relationship between the inter-related variables concisely in a few well defined coefficients viz: r_{123}, r_{1234}, r_{12345} etc.

(f) It is quite adaptable to small amount of data like that of small samples where n 30.

(g) Its reliability can be very well tested by Z test statistic developed by Prof. R.A Fisher.

(h) It helps us in testing the consistency data with reference to the value of its coefficients which is limited to ± 1.

(i) It is immensely used in making correlation analysis in various experimental designs, where inter-related variables are to be studied.