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

Probably, damped and warm air reduces inhalation oxygen quality, causing respiration deficiency and influencing the complication. n) Relationship between Rainfall and Asthma The results depicted in Table 8 reveals the relationship between mean rainfall and asthma in the study area. For the result extract, the Pearson Product Moment correlation coefficient was arrived at 0.668 . The value of the correlation coefficient by interpretation implies that there is a strong positive relationship between rainfall and asthma in the study area. This strong positive relationship was statistically significant, as indicated by the p -value (significant value) 0.002 < 0.05. Table 8: Correlation between Rainfall and Reported Cases of Asthma Variables Mean Rainfall Reported Cases of Asthma Mean Rainfall (mm) Pearson Correlation Sig. (1-tailed) N 1 16 0.668 ** 0.002 16 Reported Cases of Asthma Pearson Correlation Sig. (1-tailed) N 0.668 ** 0.002 16 1 16 *. Correlation is significant at the 0.05 level (1-tailed). Source: Author’s computation, 2021. This relation may be probably because of the heats emitted from the surface (ground) after some hours of down pour associates in impacting the disease condition via respiration. o) The Ranking Effect of Climatic Variables on Malaria/Asthma i. The Ranking Effect of Climatic Variables on Malaria Juxtaposing the result extract (see appendix iV) of the coefficients of the explanatory variables into the linear regression model postulated in chapter three of the study, we have; IM = β 0 + β 1 Mmax Temp + β 2 Mmin Temp + β 3 R Humidiy + β 4 Mrainfall +µ IM = β 0 = (-359776.1) β 1 = (17644.23) β 2 = (-8862.783) β 3 = (244.3905) + β 4 = (-40.19738) T-Statistic = β 0 = (-1.810367) β 1 = (3.157584) β 2 = (-1.561924) β 3 = (0.299275) β 4 = (-0.130316) Prob.= β 0 =(0.0976) β 1 = (0.0091) β 2 = (0.1466) β 3 = (0.7703) β 4 = (0.8987) R-squared ( r 2 ) = 0.526362 Adjusted R-squared ( Ṝ 2 ) = 0.354130 F-statistic = 3.056118 Durbin-Watson stat. = 1.595989 Prob(F-statistic) = 0.063895 The theoretical aprior expectation of the parameters coefficients is: β 1, β 2, β 3, β 4 > 0 The result summary depicts the ranking effect of climatic variables on malaria across the time frame under study with value of the coefficient of determination (also known as the R-squared [ r 2 ]) as well as the coefficient of the adjusted coefficient of determination (also known as the Adjusted r-square [ Ṝ 2 ]). The coefficient of determination (r-square: r 2 ) which by definition, is the proportion of the variance in the dependent variable (incidence of malaria in this case), that is predictable from the independent variable(s) (mean maximum temperature, mean minimum temperature, mean relative humidity, and mean rainfall), was arrived at 0.526362. This thus implies that 53% of the variation in incidence of malaria in the study area, is explained by the variation in mean maximum temperature, mean minimum temperature, mean relative humidity, and mean rainfall in the study area across 2003 and 2018. From the summary of the results, the adjusted Ṝ 2 was arrived at 0.354130. This by implication implies that over 35 percent of the total variation in the incidence of malaria is explained by the variation in the explanatory variable (mean maximum temperature, mean minimum temperature, mean relative humidity, and mean rainfall) Relationship between Climatic Variables and Incidence of Malaria and Asthma in Keffi Local Government Area of Nasarawa State, Nigeria © 2021 Global Journals 1 Global Journal of Science Frontier Research Volume XXI Issue VI Year 2021 66 ( H ) Version I