A BAYESIAN APPROACH TO NORMAL REGRESSION MODELLING WITH AGGREGATE CLIMATIC DATA

Abstract

Modeling the relationship between some climatic determinants in the wet or cropping season of Makurdi, Benue State, Nigeria requires data aggregation. The consequence of this aggregation is the reduction in data sample size. This poses serious challenge of lack of model-fit when the Classical Linear Regression Modeling Approach is employed. The Bayesian Normal Regression Modeling Approach was therefore employed in surmounting this problem. Three Bayesian Normal Regression Models were fitted namely; the Solar radiation, Total Rainfall Amount and the Number of Dry Days model. Each model result was compared with that of its Classical model counterpart. The discrepancies observed were blamed on the sample size reduction. The results of the Bayesian models revealed that; Solar radiation increases by 0.791 MJ/m² for each unit increase in the natural logarithm of Relative humidity. While it increases by 0.895 MJ/m² for each unit increase in the natural logarithm of Wind speed. Total Rainfall Amount increases by 66.280 mm for each unit increase in the natural logarithm of the Number of Dry Days while it increases by 2.912 mm for each unit increase in the natural logarithm of the Number of Wet Days. Furthermore, the Number of Dry Days decreases by 6.905 days for each unit increase in the natural logarithm of Number of Wet days, while it increases by 2.028 days for each unit increase in the natural logarithm of Total Rainfall Amount. The study affirmed that the cropping season climate of Makurdi is becoming