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These plots are known to study the interaction effects (studying the effects of two factors at one time) of the factors on the responses. Linear regression plots between the observed and predicted values of the response properties were drawn ( Fig. 7a�Cc). The linear correlation plots demonstrated high values of R-squared for all the three responses drawn between the predicted and experimental values. The R-squared values of Y1, Y2 and Y3 were found to be in the range of 0.9963�C0.9978, 0.9960�C0.9981 and 0.9942�C0.9976, respectively. The model proposes the following polynomial equation for Drug Release Y1=95.30-2.88X1+0.12X2+0.12X1X2+0.15X12-0.35X22where, Y1 is drug release, X1 is the polymer concentration, and X2 is the concentration of plasticizer. The Model F-value of 1100.58 implies the model is significant p? drug to diffuse out of the patches. 33 The drug release pattern in the fast dissolving films (FDF) is also affected by the concentration of plasticizer (X2) and followed a direct relationship when the amount of plasticizer increases. A positive value for the coefficient (above mentioned equation) is an indicative of the favorable effect whereas a negative value for the coefficient indicates an unfavorable effect. There is only a very little such as 0.01% chance that a ��Model F-Value�� this large could occur due to noise. Values of ��Prob?>?F��?0.1000 indicate the model terms are not significant. If there are many insignificant model terms, model reduction may improve your model. The ��Lack of Fit F-value�� of 0.44 implies the Lack of Fit is not significant relative to the pure error. The following polynomial equation prevailed from the model for disintegration time. Y2=26.90+8.17X1+0.33X2+2.50X1X2+4.36X12+2.86X22where, Y ?2 is disintegration time. All the formulation showed response of Y ?2??F��?