TJ Heinz Jonathan Choih Pasha Korsakov Analysis of The World Bank s Findings on Air Pollution PM10 Concentration in World Cities The World Bank is an international non governmental organization with the goal of aiding developing countries throughout the world with financial and technical assistance Besides the obvious concern of financial stability for the impoverished countries of the world the World Bank also focuses on education health infrastructure and communications Our analysis deals with the environment and infrastructure aspects of the World Bank s work The World Bank provided us with the dataset entitled Air Pollution in World Cities PM10 Concentration PM stands for particulate matter pollution in the air This dataset showed every major city in the world with a population of 100 000 or more and also every country s PM concentration The country based portion of the dataset was used for this analysis The primary determinants of PM concentrations are the scale and composition of economic activity population the energy mix the strength of local pollution regulation and geographic and atmospheric conditions that affect pollutant dispersion in the atmosphere World Bank Thanks to economic improvements throughout the world and technological advancements PM10 concentration has increased at a very slow rate The objective of this analysis was to determine the pollution concentration of several regions throughout the world including Africa Asia Australia Oceania Central America Europe the Middle East North America and South America Our original null hypothesis was that the of the pollution concentration of each region was equal Conversely the alternative hypothesis states that the of each region is not equal We used several procedures of SAS to determine whether our hypothesis would stand After importing the Microsoft Excel dataset into SAS we performed several procedures to sort the data by region in order to determine the relative pollution concentrations First we used proc sort to separate the dataset using the aforementioned regions of the world Next we performed the proc univariate procedure This derived the mean standard deviation outliers and sample t statistic for each region After this step we were able to compare each region s mean pollution concentration This showed that the Africa region was most polluted and the Europe region was the cleanest barely edging out North America These results are very understandable considering the relative developmental state of most countries in Africa and the highly technologically and industrially advanced positions of European nations The next step was to compare these region specific means to that of the entire world The mean for the world calculated to a PM10 concentration of 47 42 The next step was using the proc corr procedure to determine any sort of correlation between population and PM10 concentration This showed us several outliers however they were somewhat unexpected It could be implied that a greater population would produce more pollution however the outliers we observed were those with smaller populations yet very high pollution counts because they were underdeveloped nations this included states such as Sudan and Mali In order to truly determine the validity of our hypothesis we needed to observe the variance between two groups namely region with respect to pollution To do this we carried out a one way ANOVA procedure The results of the ANOVA gave an F Value of 9 81 significantly greater than one Also the test showed a Root MSE of 33 48 that differed significantly from the concentration Mean of 56 22 H0 Africa Asia Australia Central America Europe Middle East North America South America HA Africa Asia Australia Central America Europe Middle East North America South America Because of the significantly large F value we were able to reject the null hypothesis with an value of 05 Not only does the ANOVA test show the discrepancy between region means but it can also be physically observed when comparing the two extremes of Europe at 30 95 and Africa at 73 31 PM10 concentrations It is obvious from the results of this analysis that the world has a wide range of pollution effects Traditionally more advanced regions such as Europe and North America have pollution under control because of a stable economy and a wide array of technological resources Other regions such as Africa and Central America are struggling with pollution relative to more developed regions improvements in technology and structural shifts World Bank in the world economy are helping these regions keep air pollution to a minimum WORKS CITED 1 The World Bank http econ worldbank org WBSITE EXTERNAL EXTDEC EXTRESEARCH 0 contentMDK 20785646 pagePK 64214825 piPK 64214943 theSitePK 469382 00 html Group Contributions TJ Heinz Formatting of dataset Univariate procedure by region Correlation plot population vs PM10 concentration ANOVA procedure Pasha Korsakov Selecting dataset Formatting dataset Typing of interim and final reports ANOVA procedure Jonathan Choih Formatting dataset Input for interim report SAS programming for final report ANOVA procedure
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