Group3

XiKang B00563242

JunyiWang B00691340

RunnanWu B00577191

TianranXia B00578371

YitianYang B00644053

ECON2213the Economic Rise of China &amp 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(Real-statistics.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

&nbsp

df

SS

MS

F

Significance F

Regression

1

1.94E+14

1.94E+14

324.0149

6.47E-22

Residual

44

2.63E+13

5.97E+11

Total

45

2.2E+14

&nbsp

&nbsp

&nbsp

&nbsp

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

1578126

136034.9

11.60088

5.65E-15

1303965

1852286

1303965

1852286

NV.IND.TOTL.CD

2.66E-06

1.48E-07

18.00041

6.47E-22

2.37E-06

2.96E-06

2.37E-06

2.96E-06

Y=b0+b1x1

Theequation for the total amount of CO2 released =Intercept- industryvalue added

WhereNV.IND.TOTL represents industry value added

CO2released= 1578126- (2.66E-06)

Inthe results, the calculated (P-value) is 6.47E-22.Since thistranslates into a smaller p-value, 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

&nbsp

df

SS

MS

F

Significance F

Regression

1

1.8E+14

1.8E+14

200.3734

5.46E-18

Residual

44

3.96E+13

8.99E+11

Total

45

2.2E+14

&nbsp

&nbsp

&nbsp

&nbsp

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

7953461

382369.4

20.80046

2.15E-24

7182846

8724076

7182846

8724076

NV.AGR.TOTL.ZS

-208241

14711.14

-14.1553

5.46E-18

-237889

-178593

-237889

-178593

Y=b0+b1x1

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 (P-value) is 5.46E-18. Since thistranslates into a smaller p-value, 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

&nbsp

df

SS

MS

F

Significance F

Regression

1

1.65E+14

1.65E+14

132.7114

7.13E-15

Residual

44

5.47E+13

1.24E+12

Total

45

2.2E+14

&nbsp

&nbsp

&nbsp

&nbsp

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

-8402542

996155.2

-8.43497

9.66E-11

-1E+07

-6394924

-1E+07

-6394924

SP.POP.TOTL

0.010375

0.000901

11.52004

7.13E-15

0.00856

0.01219

0.00856

0.01219

Y=b0+b1x1

Theequation for the total amount of CO2 released =Intercept- totalpopulation

WhereSP.POP.TOT represents total population

CO2released= -8402542-0.010375)

CO2released= -8402542

=7953461+ 208241

Inthe results, the calculated (P-value) is 7.13E-15. Since thistranslates into a smaller p-value, 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):175-179. Retrieved November 24, 2015(http://www.pnas.org/content/94/1/175.abstract).

Frost,Jim. (2013). `How To Interpret Regression Analysis Results: P-ValuesAnd Coefficients | Minitab`. Blog.minitab.com.Retrieved November 24, 2015(http://blog.minitab.com/blog/adventures-in-statistics/how-to-interpret-regression-analysis-results-p-values-and-coefficients).

Janda.org,(2015). Interpretingthe correlation coefficient.Retrieved 24 November 2015, fromhttp://www.janda.org/c10/Lectures/topic04/L23-InterpretingR.htm

Real-statistics.com,(2015). MultipleCorrelation | Real Statistics Using Excel.Retrieved 24 November 2015, fromhttp://www.real-statistics.com/correlation/multiple-correlation/

Sciencedirect.com,(2015). `Factors Influencing CO2 Emissions In China`s Power Industry:Co-Integration Analysis`. Retrieved November 24, 2015(http://www.sciencedirect.com/science/article/pii/S0301421512010191).