Browsing by Author "Jolayemi, Emmanuel T."
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Nigeria’s Recent Population Censuses: A Benford-Theoretic Evaluation(University of the Witwatersrand, South Africa, 2018-08-20) Ikoba, Nehemiah A.; Jolayemi, Emmanuel T.; Sanni, Olusola O. M.Population censuses in Nigeria have been plagued with under- or over-enumeration, as well as outright manipulation. This paper examines the claim of manipulated results of Nigeria’s 1991 and 2006 population censuses. Data on both censuses were obtained from the National Bureau of Statistics and analyzed via fitting Benford’s probability distribution. The overall census data, as well as aggregate data for the six geopolitical zones of the country were examined to determine the level of conformity with Benford’s distribution, using the Chi-square goodness of fit test. The conformity analyses showed that the overall counts differed significantly from Benford's in both censuses. The North-West region had the highest deviation in both censuses, while the North-East and South-West had the lowest deviation in 1991 and 2006 censuses, respectively. Significant conformity was observed in the sizes of the local government areas and the population density for the 2006 census. It is concluded that some datasets with built-in minimum and maximum values may still conform to Benford’s distribution provided the range of values of the first significant digit span digits 1 to 9. It is recommended that census results are scrutinized on the basis of Benford’s distribution as an additional check on the quality.Item ON ASSESSING EFFICIENCY OF AN ALTERNATIVE CLASSIFIER THROUGH SENSITIVITY AND SPECIFICITY(International Centre for Advance Studies, 2017-05-01) Sanni, Olusola O. M.; Jolayemi, Emmanuel T.; Ikoba, Nehemiah A.; Adeniyi, Olakiitan I.We examine the efficiency of a competing classifier through sensitivity and specificity, utilizing a Monte Carlo Study.We observed that when sensitivity or specificity or both are low, the efficiency of such classifier is poor and not desirable. We found that even with large sample size empirical efficiency does not show any appreciable difference. Our results suggest that estimation of efficiency is not good when we have small sample sizes (< 30 ). We found that if the sensitivity or specificity or both are high (> 0.75 ), such classifier have good efficiency. This is slightly more relaxed than the results by other researchers where sensitivity and specificity of .80 or higher was recommended.