[GWA 040] Determination of causes for global warming - Neelam91/Global-Warming-Analysis GitHub Wiki
Now I would wish to understand the causes of global warming and to do so, I researched and found the following 7 variables to be worthy of further exploration.
1.Global CO2 emission data 2.Deforestation data (was assessed using the total wood production data) 3.Greenhouse gases emission by sectors 4.Renewable Energy share 5.Fossil fuels consumption 6.Livestock Production 7.Agricultural land uses
Feature Selection: Feature selection was performed by using Recursive feature elimination method and the following three variables were found to be higher degree of importance. Those variables were Green house gas emission share, average agricultural land and Renewable energy share.
Using the three variables, a linear multivariate regression model was fitted using the training data. Satisfactory levels of Adjusted R square (0.76), overall model significance(f-statistics), individual parameter significance were achieved. Further to check for any possibility of overfitting, the model was applied to an independent test data set. Root mean squared error statistics was calculated and was found to be 0.13 (lower than the advised value of 0.3). Therefore, overfitting was not found to be a concern.
Results: Ordinary least squares
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Model: OLS Adj. R-squared: 0.758
Dependent Variable: Mean_gtemp AIC: -32.5650
Date: 2020-08-29 15:54 BIC: -26.8291
No. Observations: 31 Log-Likelihood: 20.283
Df Model: 3 F-statistic: 32.25
Df Residuals: 27 Prob (F-statistic): 4.51e-09
R-squared: 0.782 Scale: 0.018165
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Coef. Std.Err. t P>|t| [0.025 0.975]
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Renewables_energy_share (%) 0.2128 0.0309 6.8804 0.0000 0.1493 0.2763
GHG emissions per capita (tonnes carbon dioxide equivalents (CO‚ÇÇe)) 0.1489 0.0705 2.1118 0.0441 0.0042 0.2936
Average_agricultural_land 0.5495 0.1485 3.7004 0.0010 0.2448 0.8543
intercept -23.7143 6.0718 -3.9057 0.0006 -36.1725 -11.2560
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Omnibus: 0.297 Durbin-Watson: 2.599
Prob(Omnibus): 0.862 Jarque-Bera (JB): 0.251
Skew: 0.193 Prob(JB): 0.882
Kurtosis: 2.787 Condition No.: 10009
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Technique used: Multivariate linear Regression analysis