MULTIPLE REGRESSION ANALYSIS OF RELATIVE HUMIDITY AND TEMPERATURE ON RAINFALL (A CASE STUDY OF METEOROLOGICAL GARDEN, FEDERAL POLYTECHNIC OFFA, KWARA STATE)
Abstract
This study examined the relationship between relative humidity, temperature, and rainfall using multiple regression analysis. Data on monthly rainfall (mm), temperature (°C), and relative humidity (%) were collected from the Meteorological Garden, Federal Polytechnic Offa, Kwara State, covering a period of three years (January 2021 to December 2023), giving a total of 36 monthly observations. The study adopted the Ordinary Least Squares (OLS) method of multiple regression to determine the individual and combined effects of relative humidity and temperature on rainfall. The results showed a strong positive relationship between relative humidity and rainfall, and a negative relationship between temperature and rainfall. The multiple correlation coefficient (R = 0.967) indicated a very strong relationship between the predictor variables and the dependent variable, while the coefficient of determination (R² = 0.935) showed that approximately 93.5% of the variation in rainfall was explained by changes in relative humidity and temperature. The F-statistic was 245.3 and was statistically significant at the 0.05 level of significance. The regression equation derived was: Rainfall = -352.4 - 8.24(Temperature) + 4.87(Relative Humidity). The study concluded that both temperature and relative humidity are significant predictors of rainfall in the study area. It was recommended that meteorological authorities should intensify monitoring of these climatic variables to improve rainfall forecasting and planning for agricultural and water resource management in Kwara State.
Keywords: Multiple Regression, Rainfall, Relative Humidity, Temperature, Meteorological Garden.
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