Applications of Some Well-Known Skewed Distributions to Greenhouse Gas Emissions Data

In many problems of transportation and environmental processes and designs, fitting of a continuous probability distribution to the greenhouse gas emissions data from cars may be helpful in predicting the probability or forecasting the frequency of occurrence of the greenhouse gas emissions from burning fossil fuel for our cars, trucks, ships, trains, and planes, and planning beforehand. The objective of this paper is to study and conduct a statistical analysis of the greenhouse gas emissions data from cars. Since our data are skewed in nature, we fit the following well known skewed distributions: 3 parameter Birnbaum-Saunders (or fatigue-life), gamma, 3 parameter gamma, generalized extreme value, 3 parameter lognormal, 4 parameter Pearson 6 and Weibull distributions. We have tested the goodness of fit these distributions to a random sample of the greenhouse gas emissions data from 32 different models of cars to determine their applicability and best fit to these data based on the Kolmogorov-Smirnov, Anderson-Darling, and Chi-Squared Goodness-of-Fit Tests.