Regression modeling approach
The data set was parsed into training and testing partitions using methods of randomization. The validity of the model was based on goodness-of-fit of R Sq. > 90% and ANOVA, [p value <0.05] and a consequent F Ratio (Table 2a and 2b).These statistical results confirmed acceptable degree of predictability of the model.
Following this multiple-regression analysis, we conducted 2,000 bootstrap samplings using the predicted coefficients and random variates from chosen intervals of parameters. The assumption for this analysis was that each of the parameters were independent variables. The coefficients of each parameter were determined by using multiple regression analyses, which is the multiplier to the parameter value in a linear regression equation. The inclusion of all the variables in analysis ensures their contribution to the model [41]. However, analysts applying this model in the future may, at their judgment, evaluate statistical significance of regression coefficients. Parameters that are not significant maybe excluded using step wise regression. In our analysis, results based on training dataset predictors matched with those from the test dataset confirming an acceptable degree of predictability of the model. We invite the readers of this article to contact us to analyze the predictive potential of the model using their data.