3. Using a sample of 26,995 cars and trucks a researcher is interested in examining the fuel efficiency of automobiles. She collects data on fuel efficiency in KM per litre (fuel_eff), weight of the automobile in kg (weight), the age of vehicle in years (age), year of purchase (year) and dummy variables for whether the vehicle is an electric vehicle (EV, taking the value 1 if the vehicle is electric and zero otherwise) and whether the vehicle is a truck or car (truck, taking the value 1 if the vehicle is electric and zero if it is a regular car). (a) Before running the regression researchers looks at the summary statistics of her variables to make sure everything seems reasonable. These summary statistics are presented below. Variable Obs fuel_eff weight EV truck year age Source Model Residual Total 26,995 26,995 26,995 26,995 26,995 fuel_eff 26,995 The results of this regression are provided below. weight EV truck year _cons SS Mean 24.56461 1555.431 1214394.72 .0412669 .4657159 2006.125 Coefficient Comment on these summary statistics. (b) Having looked at her data the econometrician decides to estimate the following regression -.0138487 8.669565 -1.388586 .2301768 df 5 Std. dev. 845455.167 4 211363.792 368939.557 26,990 13.6694908 26,994 6.707278 369.3412 .1989106 .4988325 8.043656 Std. err. fuel effi= B + Bweight+ P3EV + Patrucki + Byear + i MS 0 44.9875796 t 8.7 657.7084 Min 0 0 1994 5 P>|t| .0000756 -183.28 0.000 .1154314 75.11 0.000 .055063 -25.22 0.000 .0029554 77.88 0.000 -415.3692 5.904177 -70.35 0.000 76.4 3039.066 Number of obs F(4, 26990) Prob > F R-squared Max 1 1 2020 = Adj R-squared = Root MSE = - .0139968 8.443314 -1.496512 .2243841 -426.9417 = 5 26,995 15462.45 0.0000 0.6962 0.6961 3.6972 [95% conf. interval] -.0137006 8.895817 -1.28066 .2359695 -403.7967 Interpret these results. (c) Test the hypothesis that there is no difference in the fuel efficiency of electric vehicles compared to other vehicles. (d) Predict the fuel efficiency an electric car purchased in 2020 with average weight. (e) The researcher is interested in using the results of this regression to inform policy on climate change. What conclusions would you reach?