# Political Analysis

Emperical politicals analysis final paper:
Hypothesis: in comparison of states, states that have bigger industrial sector (make up higher percentage of its GDP) tend to have Highter national carbon footprint, than states with smaller industrial sectore (make up less percentage of its GDP).
DV:” carbon.footprint” National carbon footprint “ratio”
IV: “ciagdpin” Composition of GDP: Industrial sector “ratio”
Control: “econ.compete” Global economic competitiveness “interval”
From the “world section”
If a country is not as competitive in the global economy then their carbon foot print would be less than if they were.

Final paper notes:

• Title
• Abstract: 8-10 lines (in italics) (do not label).
• Intro (1 page) (2 paragraphs) (global warming) (global economy).
• Literature review 3 academic sources (peer reviewed). (not .com, .gov. .org) (3 paragraphs) (page and half)
• Argument and hypothesis= phrase according to template.
• Methodology: we will be using R studios to run my commands and get the results through there. 1. Explain the choice of tests, 2. Explain the variables (Independent variable, Dependent variable, Control variable), 3. Expectations from the tests. (page and half to two).
• Results: 1. Explain your result in detail, 2. Accept or reject your hypothesis. (refer to HW #3) about a page, page and a little bit.
• Conclusion is 1 page no less (2 paragraphs) (one paragraph is summary, second paragraph is discussion “what are the implications, what is the contribution, what further research can be done on the topic).
• Work cited page (the 3 peer reviewed academic sources)
• Appendix: your statistics tables (screen shots of command and result)

Results:
Without control:
1) R^2  0.1059  10.59%. this means 10.59% of change to the national carbon foot print is explained by caigdpin.

2) Y= 0.304+0.045x
Interpretation:
For every 1% increase in the size of the industrial sector as % of GDP, the national carbon footprint increases by 0.045%.

3) Significance, p<0.001 which is less than 0.05, and this shows us that the relationship between the DV and IV is significant, which means I’m supporting my working hypothesis.
With control:
Y= -7.349+0.044x(1)+1.898 X(2)
1) For every 1% increase in the size of the industrial sector, the national carbon footprint increases by 0.044%, while control is constant.

2) R^2  0.5077 50.77 %. this means 50.77% of of change to the national carbon foot print is explained by caigdpin and by econn.compete. the percentage has gone up in comparison to it R^2 produced without including the control variable. (it is explaining more).

3) After reviewing the results and looking at the P-value I still support my working hypothesis, this is because the P-value is p<0.001, which is a smaller number than 0.05, and this basically states that the relationship between my DV and IV is still significant after adding the Control variable.

Work Cited:
Chou, K., Walther, D., & Liou, H. (2019, October 14). The conundrums of sustainability: Carbon emissions and electricity consumption in the electronics and petrochemical industries in Taiwan. MDPI. https://www.mdpi.com/2071-1050/11/20/5664
Galbreath, J. (2011). To What Extent is Business Responding to Climate Change? Evidence from a Global Wine Producer. Journal of Business Ethics, 104(3), 421–432. http://www.jstor.org/stable/41476097
Hertwich, E. G., & Peters, G. P. (2009, June 15). Carbon Footprint of Nations: A Global, Trade-Linked Analysis. ACS Publications. https://pubs.acs.org/doi/full/10.1021/es803496a

Appendix:
DV:” carbon.footprint” National carbon footprint “ratio”

IV: “ciagdpin” Composition of GDP: Industrial sector “”

Simple Lenier Regression without control:

Control: “econ.compete” Global economic competitiveness “ratio”

Simple Lenier Regression with Control:

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