Path Modeling on the Effect of Climate Change in Nigeria
Chinelo Mercy Igwenagu
Abstract
Open-access reader
Chinelo Mercy Igwenagu
Abstract
Open-access reader
Climate change is one of the environmental challenges currently facing Man, Plant and Animal existence globally.Awareness of its effect on some of the climatic factor is important; as this will tell the need for urgent abatement action, especially for developing countries who might be nonchalant given the level of their economic activities.This research therefore considered data on climatic factors collected from 18 States out of the 36 state; covering the six geopolitical zones of Nigeria.These factors were analyzed using Path analysis; with correlation coefficient as the path coefficients.The effect of climate change on these factors was measured by the path coefficients obtained from the correlation analysis.Path modeling shows the order in which these factors are affected.The regression result shows some effect of multicolinearity among the variables used.However the R 2 value of 0.668 indicates that the variables used accounted for 66.8% approximately 67% of the total variability in the values of the response variable; that is accounted for by the fitted regression model.Therefore the model can be said to have a good fit, hence the variables were used for Path modeling.
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Climate change is one of the environmental challenges currently facing Man, Plant and Animal existence globally.Awareness of its effect on some of the climatic factor is important; as this will tell the need for urgent abatement action, especially for developing countries who might be nonchalant given the level of their economic activities.This research therefore considered data on climatic factors collected from 18 States out of the 36 state; covering the six geopolitical zones of Nigeria.These factors were analyzed using Path analysis; with correlation coefficient as the path coefficients.The effect of climate change on these factors was measured by the path coefficients obtained from the correlation analysis.Path modeling shows the order in which these factors are affected.The regression result shows some effect of multicolinearity among the variables used.However the R 2 value of 0.668 indicates that the variables used accounted for 66.8% approximately 67% of the total variability in the values of the response variable; that is accounted for by the fitted regression model.Therefore the model can be said to have a good fit, hence the variables were used for Path modeling.
Key concepts: Mathematics, Path (computing), Climate change, Statistics, Ecology, Biology, Computer science, Programming language