To understand the relationship between CO2 emissions per capita and GDP per capita using data from 2016, we estimated the simple linear regression Y = bo + b1X Where: Y = CO2 emissions per capita (tonnes), and X = GDP per capita ($). The results are shown below: SUMMARY OUTPUT Regression Statistics Multiple R 0.79667 R Square 0.63468 Adjusted R Square 0.63237 Standard Error 3.57480 Observations 160 ANOVA df SS MS F Sig. F Regression 1 3507.90082 3507.90082 274.50098 2.239E-36 Residual 158 2019.11241 12.77919 Total 159 5527.01322 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 0.22916 0.39527 0.57977 0.56290 -0.55153 1.00985 GDP per capita 0.00025 1.50944E-05 16.56807 2.239E-36 0.00022 0.00028 The minimum and maximum value of the two variables are provided in Exhibit 2. Exhibit 2The minimum and maximum value of the two variables are provided in Exhibit 2. Exhibit 2 GDP per capita CO2 per capita (5) (tonnes) Minimum 732.314 0.025 Maximum 113036.925 38.498 Question 2 Interpret the coefficient for the intercept in this regression model and Not yet discuss whether or not it is meaningful. answered Marked out of 1.00 Paragraph B LUT Flag questionQuestion 3 Interpret the coefficient for GDP per capita in this regression. Is the Not yet sign consistent with the relationship that you expect? answered Marked out of 1.00 Paragraph B Flag question Path: p Question 4 Using the equation and estimated coefficients above, predict the CO2 Not yet emissions per capita for Australia in 2016 if the GDP per capita is answered $44100.07 (Report the result to 2 decimal places) Marked out of 1.00 Flag question Answer:Information Exhibit 3 Flag question 45 40 35 Australia (44100.07, 16.94) . . . . ........ CO2 per capita (tonnes) ................................. 0 20000 40000 60000 80000 100000 120000 GDP per capita ($) Question 5 The actual CO2 emissions per capita for Australia in 2016 is 16.94 Not yet tonnes, as shown in Exhibit 3. Which of the following statements are answered true? (Multiple answers allowed)Question 5 Not yet answered Marked out of 1.00 V Flag question The actual C02 emissions per capita for Australia in 2016 is 16.94 tonnes, as shown in Exhibit 3. Which of the following statements are true? (Multiple answers allowed) Select one or more: C] The error in the prediction for Australia's 002 emissions per capita in 2016 is 5.69 tonnes. C] The estimated regression model overestimates Australia's CO2 emissions per capita in 2016. C] Because the error in prediction is positive, this implies that there is a positive relationship between the C302 emissions per capita and GDP per capita. C] The estimated regression model does not give us the line of best t as this particular error is not the minimum value it can be. C] The error in prediction indicates how far Australia's actual CO2 emissions is from the regression line. Question 6 Not yet answered Marked out of 1.00 \\V Flag question Question 7 Not yet answered Marked out of 1.00 \\V Flag question Using the equation and estimated coefcients above, predict the 002 emissions per capita for a country with a GDP per capita of $125,000. (Report the answer to 2 decimal places without the unit) How reliable do you think your prediction will be? Justify your answer. Suppose that we replace the independent variable of GDP per capita variable with a dummy/categorical variable of High Income where High Income = 1 if the GDP per capita > $15,000 in 2016 = 0 if the GDP per capita s $15,000 in 2016 The regression model for CO2 emission per capita as a function of dummy variable High Income is estimated below: SUMMARY OUTPUT Regression Statistics Multiple R 0.64 R Square 0.40 Adjusted R Squa 0.40 Standard Error 4.57 Observations 160 ANOVA df SS MS F Significance F Regression 1 2232.20 2232.20 107.04 1.76528E-19 Residual 158 3294.82 20.85 Total 159 5527.01 Coefficients Standard Error Stat P-value Lower 95% Upper 95% Intercept 1.72 0.47 3.67 0.000334835 0.79 2.64 High Income 7.61 0.74 10.35 1.76528E-19 6.15 9.06Question 8 Not yet answered Marked out of 1 .00 F Flag question Question 9 Not yet answered Marked out of 1 .00 P Flag question High Income [.61 0.14 10.35 1.!bZBE-19 6.15 9.0 What is the estimated 002 emissions per capita for a Non High Incom Country? (Report the answer to 2 decimal places without the unit) What is the estimated 002 emissions per capita for a High Income Country? (Report the answer to 2 decimal places without the unit) Question 10 What is the interpretation of the intercept coefficient in this regression Not yet model? answered Marked out of 1.00 Paragraph B BD Flag question Path: p Question 11 What is the interpretation of the coefficient for the High Income dummy Not yet variable? answered Marked out of 1.00 Paragraph B E Flag questionSome of the following questions were taken from past exam papers (Semester 1 2021 and Semester 2 2019). These will help you greatly in preparing for your exam. We work with data on annual CO2 emissions of countries around the world in 2016. We are interested in assessing how economic growth may affect CO2 emissions. To do so we construct a scatter plot between CO2 emissions per capita (in tonnes) and GDP per capita (in $) for data from 2016 as shown in Exhibit 1. Exhibit 1 45 40 35 30 CO2 per capita (tonnes) 10 OUT 0 20000 40000 60000 80000 100000 120000 GDP per capita ($)Question 1 Based on what the scatter plot tells us about the relationship between Not yet CO2 emissions per capita and GDP per capita, which statement is answered incorrect and not implied by the analysis. Marked out of 1.00 Select one: Flag question O The linear correlation between CO2 emissions per capita and GDP per capita is positive but the linear correlation between GDP per capita and CO2 emissions per capita is negative. O CO2 emissions per capita and GDP per capita seem to have a reasonably strong positive linear correlation. O When GDP per capita is high, CO2 emissions per capita is also generally high and vice versa. O Even if CO2 emissions per capita and GDP per capita have a strong positive linear correlation, the correlation cannot be greater than 1