Abstract
Various types of running times exist for analysis of algorithmic efficiency. This research presents a more empirical approach to the problem for the practical measurements of the actual running time of algorithms by considering a plethora of randomized inputs Rn and then fitting a regression curve in n to the algorithm of practical time complexity ν (n). This will also provide us the productivity factor η which will quantify the universal running time with respect to the asymptotic worst-case complexity and evaluate the efficiency of the given algorithm with the help of leading coefficients. This research will also help us compare similar algorithms in a mathematically modelled manner.
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