Global Journal of Science Frontier Research, H: Environment & Earth Science, Volume 21 Issue 6

after taking into consideration the degree of freedom (12) which is indeed strong. The F-statistic value of 3.056118 was low and shows the overall estimated regression model was not at the conventional significance level of 0.05 level of significance, and thus not statistically significant. This was as a result of the F-statistics (3.056118) found to be greater than the critical F-statistics probability (0.063895 > 0.05). The Durbin-Watson statistic of 1.595989, indicate the presence of positive autocorrelation in the regression. The regression results also reveals the coefficient of the constant parameter, as well as the coefficients of the explanatory variables of the regression model of the study. From the regression result, it can be observed that the constant parameter ( β 0 ) is negatively related to the incidence of malaria in the study area over the period under study with a coefficient of β 0 = -1.810367. The value of t-statistic of the constant parameter shows the statistical significance of the relationship between the constant parameter and the dependent variable of the regression model. Given that the t-statistic was arrived at -1.810367, with a probability value of 0.0976, the negative relationship between incidence of malaria and β 0 was not statistically significant, since the p- value = 0.0976 > 0.05. The results revealed a positive relationship between mean maximum temperature and the incidence of malaria in the study area, with a coefficient value of MmaxTemp = 17644.23. The t-statistic value of MmaxTemp shows the statistical significance of the relationship between MmaxTemp and the dependent variable of the regression model (incidence of malaria [ IM ]). The t-statistic of MmaxTemp was arrived at 3.157584, with a probability value of 0.0091. At 0.05% level of significance, p- value = 0.0091< 0.05. It thus implies that the positive relationship between IM and MmaxTemp is statistically significant. This result conforms to the aprior expectation of a positive relationship between IM and MmaxTemp ( β 1 > 0). The implication here is that a unit increase in MmaxTemp resulted in a unit increase in IM in the study area between 2003 and 2018. The coefficient of MminTemp shows a negative relationship between MminTemp and IM in the study area, with a coefficient value of -8862.783. The t- statistics of the coefficient of MminTemp was arrived at - 1.561924, with a probability value of 0.1466 at 0.05% level of significance. This result is against our aprior expectation of a positive relationship between MminTemp and IM ( β 2 > 0), and not statistically significant since p- value = 0.1466 > 0.05. The implication here is that an increase in MminTemp across the time series under consideration has disproportionally been met with IM , but not at a significant level. The coefficient of R Humidiy from the results was arrived at 244.3905, indicating a positive relationship between the IM and R Humidiy . The t- statistics of this explanatory variable was arrived at 0.299275, with a probability value of 0.7703 at 0.05% level of significance. This result conforms with our aprior expectation of a positive relationship between IM and RHumidiy ( β 3 > 0), but however, not statistically significant since p- value = 0.7703 > 0.05. The implication here is that an increase in RHumidiy across the time series under consideration resulted in an increase in IM , but not at a significant level. More so, the coefficient of Mrainfall as depicted in the results above was -40.19738, implying an inverse (negative) relationship between IM and Mrainfall across the time series under study. The t-statistics of the coefficient of Mrainfall was arrived at -0.130316, with a probability value of 0.8987 at 0.05% level of significance. This result does not conform to our aprior expectation of a positive relationship between IM and Mrainfall ( β 4 > 0), and was not statistically significant since p = 0.8987 > 0.05. The implication here is that and increase in the Mrainfal resulted into a decrease in the IM but not at a significant level. p) The Ranking Effect of Climatic Variables on Asthma Juxtaposing the result extract (see appendix V) of the coefficients of the explanatory variables into the linear regression model postulated in chapter three of the study, we have; IAsthm = β 0 + β 1 MmaxTemp + β 2 MminTemp + β 3 Mrainfall + β 4 RHumidiy +µ IAsthm = β 0 = (99.78362) β 1 = (-2.377939) β 2 = (-0.621961) β 3 = (0.431406) + β 4 = (-0.039827) t-Statistic = β 0 = (1.212641) β 1 = (-1.027759) β 2 = (-0.264723) β 3 = (1.275884) β 4 = (-0.311828) Prob. = β 0 = (0.2507) β 1 = (0.3261) β 2 = (0.7961) β 3 = (0.2283) β 4 = (0.7610) R-squared ( r 2 ) = 0.586996 Adjusted R-squared ( Ṝ 2 ) = 0.436813 F-statistic = 3.908536 Durbin-Watson stat. = 0.579206 Prob(F-statistic) = 0.032654 The theoretical aprior expectation of the parameters coefficients is: β 1, β 2, β 3, β 4 > 0 Relationship between Climatic Variables and Incidence of Malaria and Asthma in Keffi Local Government Area of Nasarawa State, Nigeria 1 Global Journal of Science Frontier Research Volume XXI Issue VI Year 2021 67 ( H ) © 2021 Global Journals Version I