In this paper, we develop stepwise regression data envelopment model to select important
variables. We formulate null hypothesis to understand the importance of each variable and
use Kruskal–Wallis test for this purpose. If the Kruskal–Wallis test does not reject the null
hypothesis then we can conclude that all the variables are of equal importance as their
presence and on the other hand absence of other variable does not create huge fluctuations
in efficiency scores in fact give a complete ranking relative to base model. If the Kruskal–
Wallis test does reject the null hypothesis this will imply there is significant fluctuation in
the efficiency score relative to base model. And therefore we have to further check the pair
of variables that causes the fluctuation in order to determine its importance using Conover–
Inman test. The results of the proposed models are compared with the results of previously
published models of the same dataset. The proposed models helps understand the
extent of misclassification decision making units as efficient/inefficient when variables
are retained or discarded alongside provides useful managerial prescription to make
improvement strategies.