2. he estimated coefficient for BB is b2 = 0.085 (rounded to three decimal places), which is positive as expected. In words, this estimated value implies that a unit increase in the share of households with high-speed internet access increases GDP growth per capita by 0.085% (about one-tenth of one percent). The F-test discussed in Question 2b evaluates the overall fit of the model. To examine the statistical significance of the individual model components, we can use the t-ratio and p-value associated with each estimated coefficient. For example, the t-ratio for BB is 3.995 with p-value 0.0004. Under the null hypothesis for each explanatory variable, the coefficient equals zero and does not affect GDP growth. The t-ratio increases in absolute value and the p- value declines toward zero as the strength of the evidence against the null hypothesis increases. We should reject the null hypothesis and conclude that the coefficient is significantly non-zero if the p-value is less than 0.10. Otherwise, if the p-value is larger than 0.10, we should fail to reject the null hypothesis and conclude that the coefficient is not significantly different from zero. Here, the p-value for BB is less than 0.10, so BB is a statistically significant explanatory variable for Q2GDP. Based on the p-values in the GRETL output, is the coefficient associated with CHILD significantly different from zero (i.e., p-values less than 0.10)? Please briefly explain how we should interpret the estimated value of the CHILD coefficient.

3. 

Model 2: OLS, using observations 1-32 Dependent variable: Q2GDP Excluding the constant, p-value was highest for variable 5 (BUSWEB) Model 2: OLS, using observations 1-32 Dependent variable: Q2GDP Excluding the constant, p-value was highest for variable 5 (BUSWEB)