XiKang B00563242
JunyiWang B00691340
RunnanWu B00577191
TianranXia B00578371
YitianYang B00644053
ECON2213the Economic Rise of China & India
Correlationbetween variables
Due
Correlationbetween variables
Correlationbetween CO2 emissions and agriculture value added.
Dataon the general output of CO2 emissions between1966 and 2011 wascorrelated with the output of agriculture value added. A Pearson’sr, also known as the correlation coefficient of 0.90551 wasobtained. This is considered as a very large effect since this valuelies between (0.5) and (1). Since this value is a negative (), itproves that the increased output of agriculture value added is highlyrelated to reduced CO2 emissions (Sciencedirect.com 2015). Therefore,the higher the amount of addition of value on agricultural products,the lower the rates of emissions of CO2 gas evidenced in China(Realstatistics.com, 2015). Therefore, we fail to reject thehypothesis that China’s CO2 emissions are negatively dependent onthe agriculture value added.
Correlationbetween CO2 emissions and industry value added
Datacollected on the total CO2 emissions in China between the year 1966and 2011 was tested for correlation with the independent variable ofindustry value added. A Pearson’s r (Correlation Coefficient) of0.9383 was obtained. Since this value is larger than 0.5, it showsthat the rate of CO2 emissions in China is dependent on industryvalue added. The value is a positive number and therefore, shows thatthe higher the rate of industry value additions on products, thehigher the amount of CO gas emissions are made (Janda.org, 2015).These findings resemble those of the hypothesis, and we, therefore,fail to reject the hypothesis by stating that China’s CO2 emissionshave a positive relationship with industry value added.
Correlationbetween CO2 emissions and total population
Dataon the total population of China since the year 1966 to 2011 was usedas an independent variable during a test of its correlation with theCO2 gas emissions in the country. A Pearson’s r (CorrelationCoefficient) of 0.8661 was obtained. This value showed that there isan influence of population of CO2 emitted within the country(Sciencedirect.com 2015). This is because the Pearson’s r liesbetween 0.5 and 1. Since this value is positive, it shows that anincrease in the population has an effect on the increase of CO2 gasemitted. We, therefore, fail to reject the hypothesis that indicatesthat China’s CO2 emissions have a positive relationship with thetotal population.
LinearRegression
Alinear regression showing the rate of emissions of CO2 gas in Chinaover the years was made.
Theregression value was tested against the three independent variablesthat include total population agriculture value added, and industryvalue added. The results are as shown below
a.Regression analysis of the relationship between the amount of CO2 gasreleased and industry value added
SUMMARY OUTPUT 

Regression Statistics 

Multiple R 
0.938317 

R Square 
0.88044 

Adjusted R Square 
0.877722 

Standard Error 
772802.6 

Observations 
46 

ANOVA 

  
df 
SS 
MS 
F 
Significance F 

Regression 
1 
1.94E+14 
1.94E+14 
324.0149 
6.47E22 

Residual 
44 
2.63E+13 
5.97E+11 

Total 
45 
2.2E+14 
  
  
  

  
Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 
Upper 95% 
Lower 95.0% 
Upper 95.0% 
Intercept 
1578126 
136034.9 
11.60088 
5.65E15 
1303965 
1852286 
1303965 
1852286 
NV.IND.TOTL.CD 
2.66E06 
1.48E07 
18.00041 
6.47E22 
2.37E06 
2.96E06 
2.37E06 
2.96E06 
Y=b_{0}+b_{1}x_{1}
Theequation for the total amount of CO2 released =Intercept industryvalue added
WhereNV.IND.TOTL represents industry value added
CO2released= 1578126 (2.66E06)
Inthe results, the calculated (Pvalue) is 6.47E22.Since thistranslates into a smaller pvalue, there is a high probability thatthe results were not obtained by chance (Frost 2013).
b.Regression analysis of the relationship between the amount of CO2 gasreleased and agriculture value added
SUMMARY OUTPUT 

Regression Statistics 

Multiple R 
0.90551 

R Square 
0.819948 

Adjusted R Square 
0.815856 

Standard Error 
948362.3 

Observations 
46 

ANOVA 

  
df 
SS 
MS 
F 
Significance F 

Regression 
1 
1.8E+14 
1.8E+14 
200.3734 
5.46E18 

Residual 
44 
3.96E+13 
8.99E+11 

Total 
45 
2.2E+14 
  
  
  

  
Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 
Upper 95% 
Lower 95.0% 
Upper 95.0% 

Intercept 
7953461 
382369.4 
20.80046 
2.15E24 
7182846 
8724076 
7182846 
8724076 

NV.AGR.TOTL.ZS 
208241 
14711.14 
14.1553 
5.46E18 
237889 
178593 
237889 
178593 

Y=b_{0}+b_{1}x_{1}
Theequation for the total amount of CO2 released =Intercept totalagricultural output value added.
WhereNV.AGR.TOTL represents total agricultural output value added
CO2released= 7953461 (208241)
=7953461+ 208241
=8,161,702
Inthe results, the calculated (Pvalue) is 5.46E18. Since thistranslates into a smaller pvalue, there is a high probability thatthe results were not obtained by chance (Frost 2013)
c.Regression analysis of the relationship between the amount of CO2 gasreleased and total population
SUMMARY OUTPUT 

Regression Statistics 

Multiple R 
0.866606 

R Square 
0.751006 

Adjusted R Square 
0.745347 

Standard Error 
1115242 

Observations 
46 

ANOVA 

  
df 
SS 
MS 
F 
Significance F 

Regression 
1 
1.65E+14 
1.65E+14 
132.7114 
7.13E15 

Residual 
44 
5.47E+13 
1.24E+12 

Total 
45 
2.2E+14 
  
  
  

  
Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 
Upper 95% 
Lower 95.0% 
Upper 95.0% 
Intercept 
8402542 
996155.2 
8.43497 
9.66E11 
1E+07 
6394924 
1E+07 
6394924 
SP.POP.TOTL 
0.010375 
0.000901 
11.52004 
7.13E15 
0.00856 
0.01219 
0.00856 
0.01219 
Y=b_{0}+b_{1}x_{1}
Theequation for the total amount of CO2 released =Intercept totalpopulation
WhereSP.POP.TOT represents total population
CO2released= 84025420.010375)
CO2released= 8402542
=7953461+ 208241
Inthe results, the calculated (Pvalue) is 7.13E15. Since thistranslates into a smaller pvalue, there is a high probability thatthe results were not obtained by chance (Frost 2013).
References
Dietz,Thomas, and Eugene A. Rosa. (1997). `Effects of Population AndAffluence On CO2 Emissions`. Proceedingsof the National Academy of Sciences94(1):175179. Retrieved November 24, 2015(http://www.pnas.org/content/94/1/175.abstract).
Frost,Jim. (2013). `How To Interpret Regression Analysis Results: PValuesAnd Coefficients  Minitab`. Blog.minitab.com.Retrieved November 24, 2015(http://blog.minitab.com/blog/adventuresinstatistics/howtointerpretregressionanalysisresultspvaluesandcoefficients).
Janda.org,(2015). Interpretingthe correlation coefficient.Retrieved 24 November 2015, fromhttp://www.janda.org/c10/Lectures/topic04/L23InterpretingR.htm
Realstatistics.com,(2015). MultipleCorrelation  Real Statistics Using Excel.Retrieved 24 November 2015, fromhttp://www.realstatistics.com/correlation/multiplecorrelation/
Sciencedirect.com,(2015). `Factors Influencing CO2 Emissions In China`s Power Industry:CoIntegration Analysis`. Retrieved November 24, 2015(http://www.sciencedirect.com/science/article/pii/S0301421512010191).