A multiple regression analysis was carried out independently for each month using the elevation and the semi-sky-view factor as independent explanatory variables. A step-wise procedure was used for the regression parameter estimation. Particularly, the semi-sky-view factor was firstly regressed as independent variable and then the elevation was added. A t-test was carried for each step of the regression procedure and only parameters statistically significant at 5% level were further considered.
The most important explanatory variable is the semi-sky-view factor, which is statistically significant for all the months. This explanatory variable is able to explain from a minimum of 13% of the spatial variability (February) to a maximum of 45% in June. On the other hand, the elevation showed to be statically significant just from March to August and associated explained variance is considerable lower than for the semi-sky-view factor: values ranges from a minimum of 9.7% in August to a maximum of 15% in June. When considering both explanatory variables, explained variance ranges from a minimum of 13.2% in February to a maximum value of 46.7% in June, with most part related to the semi-sky-view factor, which is negatively correlated to the monthly solar radiation data.