As a result, all values of Ï that are close to 1 are signs of inadequate fitting of models. The autocorrelation coefficient (Ï) is a statistical measure, which implies that the independent value of Ï does not supply adequate information on the subject of â€˜goodness of fit.â€™ It is imperative for the statistic to be slotted into a test that determines the goodness of fit for it to yield meaningful information.
Several studies have looked into the Ï statistic as a valuable instrument for evaluating the goodness of fit following the tallying of biokinetic models with information on bioassays. These studies have brought to light the fact that the Ï test enables the unprejudiced detection of partiality in the succession of residuals following the fitting of a poor archetype to a series of data.
The autocorrelation coefficient test does not take into consideration the degree of miscalculations that are made by the evaluator. The chi-square (Ï‡2) test, on the other hand, is susceptible to the slightest inaccuracies made by the evaluator. Another point worth noting is that it is impossible to employ the Ï test in instances where a given data array contains less than four values (Puncher 2007).