Interpretation of multiple regression Table 2 shows the results for a regression of GDP per capita (log) on seven variables at the country level Table 2 Regression of GDP per capita on country characteristics Multiple R 0.79 R Square 0.63 Adjusted R 0.59 0.70 ANOVA MMS 72.5 8.07 42.54 115.14 Regression 9 16.49 0.00 87 0.49 95 Coefficients Standard Stat P Lower 95% Upper 8.9626 0.0224 0.0519 0.0287 0.1300 0 0077 0.0229 0.0101 1.4250 0.6378 Intercept 8.1096 0.4291 18.8978 0.0000 7.2567 GFCF -0.0044 0.0135 -0.3248 0.7461 -0.0312 GOV EXP 0.0191 TAXTOT 0.0128 0.0733 n 0n4A -0.0040 0.0100 509 0.2000 -0.0137 0.0080 5984 0.1136 -0.0031 0.0283 26107 0.0106 0.0178 TOTUN 0.013 -0.2974 0.7689 -0.0310 -0.0097 0.0099 -0.9745 0.3325 -0.0294 0.2213 4.4524 0.0000 0.5454 0.2181 0.9372 03513 -0.2291 0.9852 Asia 0.2044 You are required to a. Write down the estimated equation for the model estimated in this regression b. Interpret the results also in terms of relationship between dependent and explanatory variables. Does the model explain GDP per capita? Which variables explain GDP per capita? Which do not? c. Look at the graph below about the residuals vs fitted Y For which assumption of the linear model is this graphical analysis used? Do you think that this regression output violates this specific assumption or not? Justify your answer Regression residuals vs fitted Y 2.00 1.50 1.00 0.50 0.00 8.00..' 9.0d.- 9.0010.00 1.00 12.00 .10.00 -...1.00 12.00 1.00 1.50 -2.00 Predicted Y