(TIỂU LUẬN) business statistic assignment 3 group report part 1 multiple regression

Business statistic- Assignment Group report Course name Course code Campus Group Business Statistic ECON1193 Saigon South Team CO2-2 Hoang Anh Thu_s3754971 Members Duong Nguyen Thao_s3751352 Dau Thi Hong Linh_s3763065 Lecturer Sienney Liu Table of Content: Part 1: Multiple Regression :……………………………………………………………… Backward elimination (Appendix)………………………………………… Final Model………………………………………………………………… 3 Team Regression Conclusion …………………………………………… Part 2: Time Series: ……………………………………………………………………… Time Series Trend Model (Appendix)…………………………………… Time Series Trend Model recommendations…………………………… Time Series Trend conclusion …………………………………………… Part 3: Overall Team Conclusion :…………………………………………………… Part 4: Reference List: ……………………………………………………………………… Part 5: Appendix :……………………………………………………………………………… Part 1: Multiple Regression 1.2: Final Model Dependent Variable (DV) Y: CO2 emissions (metric ton per capita) Independent Variable (IV) X1: The Gross National Income (GNI) per capita in USD X2: Renewable electricity output (% of the total electricity output) X3: Air transport, freight (million ton-km) X4: Air transport, passengers carried Data set I: All countries (ALL)  Regression output:  Multiple regression equation: Y = 4.018+ 0.0001X -0.049X  Interpret the regression coefficient of the significant independent variables: b = 0.0001: This means for each dollar of GNI per capita increase, Atlas method, CO2 emissions will increase with 0.0001 b = -0.049: This means for each percent increase in renewable electricity output, CO2 emissions will decrease with -0.049  Interpret the coefficient of determination: R = 0.662 => 66.2 % 66.2% of the variation in GNI per capita and renewable electricity output can be explained by the variation in CO2 emissions, and the remaining 33.8 % is due to other factors which we not take into consideration 2 Data set II: Low-income Countries (LI):   Regression output: In low-income countries dataset, there is no final model which is shown by the backward elimination process included in the appendix In conclusion, none of the independent variables can be explained for the change in CO2 emissions in low-income countries Multiple regression equation: There is no insignificant independent variable, we cannot build an equation for this data set Data set III: Low-middle-income Countries (LMI): Multiple regression equation: Y = -0.317+ 0.001X   Interpret the regression coefficient of the significant independent variables: b = 0.001: This means for each dollar increase in the GNI per capita, CO2 emissions will increase with 0.001 Interpret the coefficient of determination: R = 0.533 => 53.3 %  53.3% of the variation in GNI per capita can be explained by the variation in CO2 emissions, and the remaining 46,7% is caused by other factors that we not take into consideration DATA SET IV: UPPER-MIDDLE INCOME COUNTRIES:  Final regression equation: Y = 5.468-0.050X2 + 0.0002X4  Interpretation of the regression coefficient of Renewable electricity output and Air transport, freight: b2 = -0.050 is the regression slope coefficient This means for every percentage renewable energy output, the level of CO2 released will decrease by 0.050 b4 = 0.0002 is the regression slope coefficient This means for every kilometer of air transport freight, the level of CO2 released will increase by 0.0002 Interpretation of the coefficient of determination:  As the coefficient of determination (R Square) = 44.3% (0.443), 44.3% of the variation in level of CO2 released by a country is due to the variation of the renewable that country can generate and the kilometer of air transport freight Meanwhile, the remaining 55.7% may be due to other factors which not take into account DATA SET V: HIGH INCOME COUNTRIES:  Final regression equation: Y = 12.475-0.097X2  Interpretation of the regression coefficient of Renewable electricity output and Air transport, freight: b2 = -0.097 is the regression slope coefficient This means for every percentage renewable energy output, the level of CO2 released will decrease by 0.097 Interpretation of the coefficient of determination: As the coefficient of determination (R Square) = 15.3% (0.153), it can be seen that 15.3% of the variation in level of CO2 released by a country is due to the variation of the renewable that country can generate Meanwhile, the remaining 84.7% may be due to other factors which not take into account  1.3 TEAM REGRESSION CONCLUSION: By analyzing the p-value of each variable in datasets of 138 countries given above with the same Significance level (α=0.05), it proved that three models of all countries, upper-middle, and high-income countries have the same significant independent variable that is renewable electricity output (% of total electricity output) Besides, the regression of low-income countries does not have any significant independent variable while the regression of all and upper-middle income countries show one more significant independent variable as GNI and Air transport, freight, respectively As a consequence, the renewable electricity output shows a strong influence on CO2 concentration rate It is vital to understand that renewable electricity becomes significant variable since it helps cut on carbon emissions (United Nations 2018) This is due to the fact that electricity used to generate from fossil fuel considering as the second largest factor emit co2 concentration to the environment In addition, based on the ideas of Planete Energies (2016), coal has a carbon impact 20 times greater than renewables Hence, replacing fossil fuel in electricity production by other renewable energy sources will reduce the flow of CO2 emissions to the atmosphere Under those circumstances, it can be concluded that renewable electricity output plays a decisive role in reducing electricity’s environmental footprint It has been shown that upper-middle income group has the highest coefficient of determination (R squared = 44.3%) compared to all, lower-middle income, high income ratios as 43.8%, 28.4%, and 15.3%, respectively It means the regression models of upper-middle income countries would describe the best estimation among given data sets Speaking about this, R squared is a statistic that usually ‘interpreted as summarizing the percent of variation in the response that the regression model explains’ (Ford, C 2015) That is to say, R squared = 0.443 means 44.3% of the model’s alternation will be explained in our dependent variable, in this case is CO2 emissions Part 2: Time Series 2.2: Time Series Trend Model recommendations: The calculation of the errors, MAD, SSE and Trend Model Recommendation In order to make the best prediction for CO2 emissions, it is necessary to scrutinize which trend model is the most suitable for each country by identifying the smallest in both MAD and SSE Based on the calculation and the comparison, these figures are highlighted in the table above to be equivalent to the trend model recommendation for each country 2.3 Time Series Trend conclusion: The line chart above indicates the CO2 emissions amount in four countries inclusive of Sierra Leone, Jordan, Maldives, and Argentina over 28 years, from 1986 to 2014 Overall, it is seen that the same trend in CO2 emissions has not occurred among the given countries In more specific, Jordan and Argentina accounted for the highest amount of CO2 emissions at the beginning of the observed period (1986) with approximately 3.5 metric tons per capita, in contrast, two residual countries Sierra Leone and Maldives had the lowest CO2 emissions with 0.2 and 0.4 metric tons per capita, respectively Until the end of this period, the CO2 emissions in Maldives and Argentina tended to increase significantly, a slightly decreasing trend was witnessed in Jordan and the trend for Sierra Leone seemed to be unchanged It is worth notice that the CO2 emissions in the Maldives in 2014 gone up beyond the first level which equals CO2 emissions in Jordan at the same time By identifying the trend models of all countries; Sierra Leone, Maldives, and Argentina are three nations following the linear trend model, while there is just Jordan pursuing the Quadratic trend model To sum up, based on the time series analysis, the majority of the smallest figures of MAD and SSE fall into the Linear trend model, additionally, it is followed by most numbers of countries (3 out of 4) Therefore, the Linear trend model is considered to be the best predictor for CO2 emissions Part 3: Overall Team Conclusion In term of our analysis, our team have 95% confidence that there is a significant relationship between CO2 emissions and Gross Nation Income (GNI per capita) According to the https://www.ucsusa.org/global-warming/science-andimpacts/science/each-countrys-share-of-co2.html, it is proved that the developed countries has higher CO2 emissions rather than developing countries, which indicated the strong relationship between CO2 emissions and GNI per capita Based on the multi regressions we have already consolidated that CO2 emissions are strongly influenced by different significant independent variables at different level income of the countries, the evidence of which can be seen in high-income countries, upper-middle income countries, low-middle income countries and low income countries To be more specific, the main factors that affect CO2 emissions is renewable electricity output in high-income countries As for upper-middle-income countries, 44.3% of variation in CO2 emissions can be explained by variation in renewable electricity output and air transport, freight In low-income countries, the variables in the dataset are nonsignificant so that CO2 emissions may caused by other factors that we not take into consideration In 2030, the CO2 emission around the world will increase significantly We are 95% confident that the CO2 emissions all around the world in the future will go up based on the hypothesis test on the Assignment In Assignment 3, Linear trend model is the most reliable method to predict CO2 emissions Hence, we can forecast the CO2 emissions in 2039 of Argentina will be … , which increase compared to the prediction in 2014 (4.56 metric ton per capita) … (2019) have stated that the main factors that driving for CO2 emissions in the future is income, moreover, CO2 emissions in global in the future is significant https://www-sciencedirectcom.ezproxy.lib.rmit.edu.au/science/article/pii/S0048969718332972 According to United Nation ( 2018) , the global warming are increasing alarmingly, the total annual greenhouse emissions including from land use change reach the highest (53.5 gigaton) in 2017; they stated that the CO2 emissions in 2030 will be reduced since there are some policy that government will implement https://news.un.org/en/story/2018/11/1026691 Moreover, UN Environment proved that Forest fires, dust storms, volcanic eruptions, pollen dispersal, sea spray, evaporation of organic compounds and natural radioactivity are some of the driving factors that lead to global warming and CO2 emissions increase; some countries has enacted their own law to address land expansion and fire forest https://wedocs.unep.org/bitstream/handle/20.500.11822/25370/foresight_brief_007.pdf? sequence=1&isAllowed=y Changing government policy which are our recommendations such as increasing the tax for the fossil fuel energy and having more control in the land in the forest Consequently, we would suggest those factors for further research as they may have a significant influence on CO2 emissions, so that we can have a more accurate and reliable result Part 4: Reference List: Part 5: Appendix ... …………………………………………… Part 3: Overall Team Conclusion :…………………………………………………… Part 4: Reference List: ……………………………………………………………………… Part 5: Appendix :……………………………………………………………………………… Part 1: Multiple Regression 1. 2:... countries (ALL)  Regression output:  Multiple regression equation: Y = 4. 018 + 0.0001X -0.049X  Interpret the regression coefficient of the significant independent variables: b = 0.00 01: This means... in the GNI per capita, CO2 emissions will increase with 0.0 01 Interpret the coefficient of determination: R = 0. 533 => 53. 3 %  53. 3% of the variation in GNI per capita can be explained by the