Table 3:

Regression equations of the best model for “DDDs per 1000 inhabitants per day “in each of the parenteral anti-diabetes drugs and classifications (insulins)

DIDs for Total Insulins - Regression Analysis
Drug Name	Regression Type	Regression Equation	R2
Insulin (beef)	Polynomial	y = 0.0042x2 − 0.0573x + 0.1851	R2 = 0.7949
Insulin aspart	Polynomial	y = −0.0056x2 + 0.389x − 0.6532	R2 = 0.9936
Insulin glargine	Polynomial	y = −0.0039x2 + 0.2731x − 0.457	R2 = 0.9952
Insulin (human)	Polynomial	y = 2E-05x2 + 0.0335x + 0.622	R2 = 0.6686
Combinations Insulin	Exponential	y = 2.282e−0.052x	R2 = 0.6291
Insulin detemir	Polynomial	y = −0.0001x2 + 0.0101x − 0.0093	R2 = 0.7548
Insulin glulisine (Pen)	Polynomial	y = −0.0001x2 + 0.0121x − 0.0303	R2 = 0.9754
Insulin glulisine (Vial)	Polynomial	y = 1E-05x2 − 0.0004x + 0.004	R2 = 0.0866
Total Insulins	Polynomial	y = −0.0073x2 + 0.5802x + 2.0044	R2 = 0.988

DIDs for Total Insulin Classifications - Regression Analysis
Drug Name	Regression Type	Regression Equation	R2
Short-Acting Insulin (Vials)	Polynomial	y = 0.0009x2 − 0.0942x + 3.1181	R2 = 0.4127
Rapid-Acting Insulin (Vials)	Polynomial	y = −6E-06x2 + 0.0001x − 0.0002	R2 = 0.6849
Rapid-Acting Insulin (Pens)	Polynomial	y = −0.0057x2 + 0.401x − 0.6835	R2 = 0.9936
Long-Acting Insulin (Vials/Pens)	Polynomial	y = 0.0067x2 + 0.1777x − 0.2962	R2 = 0.9701
Total Insulins	Polynomial	y = −0.0073x2 + 0.5802x + 2.0044	R2 = 0.988