@article{MAKHILLJMMS20137428192,
    title = {Statistical Modeling of Global Warming},
    journal = {Journal of Modern Mathematics and Statistics},
    volume = {7},
    number = {4},
    pages = {41-46},
    year = {2013},
    issn = {1994-5388},
    doi = {jmmstat.2013.41.46},
    url = {https://makhillpublications.co/view-article.php?issn=1994-5388&doi=jmmstat.2013.41.46},
    author = {Igwenagu Chinelo},
    keywords = {CO2 emission,global warming,multicollinearity,modeling,principal component},
    abstract = {The problems associated with global warming, ranging from 
  increase in global temperature change in agricultural yields, glacier retreat, 
  species extinction, increase in the ranges of diseases and disease vectors were 
  reviewed. These underscore the need to reduce emission which causes global warming. 
  The proposed method of emission reduction is by emission trading according to 
  the Kyoto protocol. If this proposal holds for countries to participate actively, 
  it is important to build a model for estimating their level of CO<SUB>2</SUB> 
  emission. The aim of this study is to develop an exploratory model of global 
  warming, using CO<SUB>2</SUB> emission as a surrogate. This was done using regression 
  analysis and principal component analysis to explore some possible factors that 
  could cause global warming and to know their actual contributions. The regression 
  analysis result with a p&lt;0.001 indicates that CO<SUB>2</SUB> emission is 
  related to some of the input variables used. However due to the effect of multicollinearity 
  among the variables used, supervised principal component regression analysis 
  was used and the result of the analysis shows that model built on this method 
  gave a good fit.}
    }