Coronavirus disease 2019 (COVID-19) is one of the most infectious diseases and one of the greatest challenge due to global health crisis. The virus has been transmitted globally and spreading so fast with high incidence. While, the virus still pandemic, the government scramble to seek antiviral treatment and vaccines to combat the diseases. This study was conducted to investigate the influence of air pressure, air temperature, and relative humidity on the number of confirmed cases in COVID-19. Based on the result, the calculation of reproduced correlation through path decompositions and subsequent comparison to the empirical correlation indicated that the path model fits the empirical data. The identified factor significantly influenced the number of confirmed cases of COVID-19. Therefore, the number of daily confirmed cases of COVID-19 may reduce as the amount of relative humidity increases; relative humidity will increase as the amount of air temperature decreases; and the amount of air temperature will decrease as the amount of air pressure decreases. Thus, it is recommended that policy-making bodies consider the result of this study when implementing programs for COVID-19 and increase public awareness on the effects of weather condition, as it is one of the factors to control the number of COVID-19 cases.
The present study aims at exploring predictors influencing mathematics performance. In particular, the study focuses in four subject’s components such as motivation, attitude towards mathematics, learning style, and teaching strategies. A sample of 240 students from Agusan del Sur State College of Agriculture and Technology (ASSCAT) were involved in the study. Path analysis was used to test the direct and indirect relations between the predictors and mathematics performance. Based on the result, the calculation of reproduced correlation through path decompositions and subsequent comparison to the empirical correlation indicated that the path model fits the empirical data. Results also revealed that a large proportion of mathematics performance can be directly predicted from attitude towards mathematics, learning style, and teaching strategies. Moreover, attitude towards mathematics, learning style, and teaching strategies influence mathematics performance in direct and indirect ways.
In this article, Inverse Paralogistic distribution based on Farlie-Gumbel-Morgenstern (FGM) copula is introduced. Derivations of exact distribution V = XY ,W = X/Y , and Z = X/(X + Y ) are obtained in closed form. Corresponding moment properties of these distributions are also derived. The expressions turn out to involve known special functions.
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