Question

Carbon dioxide (CO2) emissions are widely believed to be a driver of global climate change. In this problem set you will use cross-section data to test what drives countries’ “carbon footprints,” that is, their CO2 emissions. Is it population, or is income the bigger culprit?

The data set “CO2 by country 2010 sh S17” contains data on a sample of countries’ CO2 emissions, in kilotons; population, in millions; and gross national income (GNI), in millions of US dollars, for the year 2010.

3)Based on your findings, what would you conclude is the main driver of countries’ carbon footprints—population or income? Please explain.

4)Based on your regression model, do income and population explain the difference in observed CO2 emissions between China and the United States? Explain.

5)Compare the R-squared from the simple and multiple regression. What explains the difference between the two

Please help answer 3,4,5 specificy after solving the data. Thank you.

CO2(kt) POP(millions GNI(Millions of US$) Country Name United States Afghanistan Albania Algeria Angola Antigua and Barbuda Argentina Armenia Austria Azerbaijan Bahamas, The Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil 5433056.54 8236.08 4283.06 123475.22 30417.77 513.38 180511.74 4220.72 66897.08 45731.16 2464.22 24202.20 56152.77 1503.47 62221.66 108946.57 421.71 5188.81 476.71 15456.41 31125.50 5232.81 419754.16 309.33 15170300.00 15998.78 2.8611807.46 37.06 160996.42 19.55 73946.34 1104.05 40.37451417.86 9718.57 8.39393108.52 49435.84 7702.50 1.25 23340.38 151.13 124617.10 4321.85 9.49 54058.30 10.92 493427.31 1258.61 6508.31 1497.42 18785.53 3.85 17126.87 28.40 0.09 2.96 9.05 0.36 0.28 0.31 9.51 0.72 10.16 1.97 195.212104398.02

Answer 1

Regression Stotistics 4 Multiple R 5 R Square 6 Adjusted R Square 0.93848285 0.996968002 0.996664803 64987 87815 23 Standard Error s Observations 0 ANOVA MS cance F Regression Residual 2.77746E 413 84468486136 2.7859E+13 1.38735-13 8288.156 654581 26 20 4223424307 : Total Coefficients Standard Error t Stat p-value Lowe,95% Upper 95% ntercept x Varlable 1 X Variable 2 11860,83743 1024.213091 0.376485942 15505.28969 326.6369981 0.00794775 0.764954262 0.45323120482.63002 3.135630983 0.005206 -1705.565928 5.125-221705. 56592841201.30488 0.393064658 342.8602548 0.359907227

Y= -1024.213x1+0.37648x2+11860.83743

1 SUMMARY OUIPUT Regression Statistics 4 Multiple R 5 R Square 6 Adjusted R Square 7 Standard Error s Observations 9 10 |ANOVA 0.81042496 0.656/88616 0.610445217 674767.4573 23 vero tor t... e the MS 1.82975E+13 4.55311 Significunce F 12 ession 1 1.82975E413 21 9.56153E +12 40.18678167 2.16022E 06 e last er 15. 13 Residual 15 16 17 Intercept 18 X Variable 1 19 20 21 Coefficients Standard Error t Stat upper 95% p-volue 0.343069396 Lower 95% 95.0-pper 174117.6757 -478558 14 15769.93594 7979 059 157 over y D1.. y01.- 152220.3966 156922.433 478558.469

Regression equation for co2 and population

Y= 11874.49764x1-152220.3966

regression equation for Co2 and GNI

SUMMAR OUTPUT Regression Stotistics Multiple R R Square 0.99773616 0.995477445 0.995262085 77457 81068 23 G Adjusted R Square 7 Standard Error s observations 10 ANOVA 5s MSF Significance F 12 Regression 13 Residual 14 Total 15 2.7733E413 2.77E+13 4622.393 4.15041E 26 1.259945+11 6+09 21 Coefficients standard Error tstat p.value tower95% Upper 95% ,pper 95.0 16731.6621 0.52498 0.605094 43579.22263 2601156941 43579.22253 26011.57 1 tower 950% 17 Intercept 18 x Variable 1 -8783.826612 0.355710729 0,00523149 67.98818 4.156-26 0344830295 0.366591164 0.344830295 0.36659 15

Y= 0.355x1-8783.827

3) The main drive of Co2 emission is due to population If you see that second mage the value of x1 coefficient or population coefficient is 11874.49764 means if there is one unit increase in x1 then there will be 11874.49764 unit increase in y if intercept is zero.

4)For usa Y= -1024.21*309.33+15170300*0.3767= 5394584.857

But data for china is not given here.

5) rsquared for multiple regression is 0.998485

R squared for simple population is 0.8104

R squared for simple GNI is 0.9977

Since the r squared value for multiple regression model is higerh than the rest two model .. so we can say model is correct also it says that carbon dioxide emission depends on both population and gross national income.

It means that 99.84% indicates that the model explains 99.84% variability around its mean..