Table 7.

Multiple variable regression analyses based on Durbin-Watson for the electricity generation side and chosen independent weather variables $\left(x_1,x_2,x_3\right)$.

Variables
$\mathrm{y}$	Electricity generation (dependent variable)
$x_1$	Daily solar radiation - tilted.
$x_2$	Horizontal monthly radiation
$x_3$	Average Monthly Temperature
Method	Daily
Weighted	Yes
Regression results
Number of observations:	36.000
Number of iterations:	12.000
Sum of residuals:	178.980
Average residual:	4.972
Residual sum of squares - Absolute:	13914332.921
Residual sum of squares - Relative:	13771108.977
Standard error of the estimate:	656.009
Coefficient of multiple determination (R2):	0.951
Coefficient of multiple determination – Adjusted (Ra2):	0.947
Root-mean-square error (RMSE):	659.411
Coefficient of variation of the RMSE:	0.073
F-test (p-value):	0.000
Net determination bias error (NDBE):	0.001
Durbin-Watson statistic:	1.37
Coefficient results
Name	Value	Stand error	t-ratio	p-value	User-defined
a	803.854	410.6	1.95	0.059	803.85
b	399.940	330.8	1.21	0.235	399.94
c	94.619	23.9	3.95	0.00039	94.62
d	1834.18	746.6	2.45	0.019	1834.18
Equation:	$\mathrm{y}=\mathrm{a}{x}_1+\mathrm{b}{x}_2+\mathrm{c}{x}_3+\mathrm{d}$