One of the problems faced by researchers in the field of quantitative economics relates to choice of appropriate functional forms from amongst many that can be estimated on the basis of available data on a given set of causal and effect variables. Most economic phenomena could alternatively, be stated alternatively as an effect variable y depending upon k causal variables, namely x1, x2, …. and xk. A good understanding of economic reasoning both in theory and practice will help a lot to specify, define and quantity the above mentioned variables but it seldom comes to one's rescue while one battles to understand the mode of dependence between y and x variables. The coefficient of multiple correlations is usually used as a summary statistic for comparing the explanatory power to alternative functional forms. Wisdom of the use of the statistic in case of explanatory power of models that involve dependent variable in terms of different dimensions has been questioned by several authors and in this article a simple method is suggested to overcome this problem by way of suggesting the statistic that can always be calculated no matter what is the functional form. The significance of suggested approach is empirically illustrated with the help of three models.