The R-squared statistic measures the percent of the variation in the dependent variable (GDP 19) that is explained by the fitted base model, so higher values are preferred. As long as the model includes an intercept or constant term (const), the R-squared statistic takes values between zero and one. We can always increase R-squared by adding new variables to the model, even if they do not have any reasonable relationship to GDP 19. The adjusted R-squared statistic accounts for this drawback of R-squared, and its value only increases if a new variable has a substantive impact on the model fit. The F statistic can be used to test the null hypothesis that all of the explanatory variables (except const) has a regression coefficient equal to zero (i. e., the model has no explanatory power for RGDP19). If this is not true (i. e., we reject the null hypothesis) based on the fitted model, then the model has some explanatory power for RGDP19. The p-value measures the strength of the evidence, and we should reject the null hypothesis if the observed p-value is less than 0.10. Note that GRETL expresses very small numbers in scientific notation. Here, 5.70e-10 equals 5.70 X 10-10 or 0.00000000057. Based on the observed evidence from the base model, what do we conclude from the R-squared statistic and the F-test statistic?