STUDY OF MULTICOLLINEARITY ON THE EFFECT OF CLIMATIC CONDITIONS ON OIL PALM YIELD AT NIFOR NIGERIA

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STUDY OF MULTICOLLINEARITY ON THE EFFECT OF CLIMATIC CONDITIONS ON OIL PALM YIELD AT NIFOR NIGERIA (STATISTICS PROJECT TOPICS AND MATERIALS)

ABSTRACT

One of the major problems of multiple linear regression analysis is multicollinearity of the independent variables. The existence of multicollineariity on climate variables such as relative humidity, solar radiation, rainfall, sunshine and temperature on the response of agricultural output may lead to inflation of standard error of the regression coefficients or false non-significant p-value. In this study, monthly data spanning from 1980-2012 obtained from the Nigeria Institute for Oil Palm Research (NIFOR) on relative humidity, solar radiation, rainfall, sunshine, temperature and oil palm yield were used to examine the probable effects of climate conditions/climate change would have on oil palm yield. The estimation of parameters of climatic variables in multiple linear regression appears to have suffered severe distortions due to multicollinearity. This research study resort to principal component regression, ridge regression and stepwise regression to stabilized the parameter estimate. Ridge regression was used to estimate the effect of climate conditions on oil palm yield because it performed better than others due to its lower measure of accuracy. It was observed that average relative humidity and rainfall had positive significant effect while solar radiation, mean sunshine hour and average air temperature had negative significant effect on oil palm yield.

CHAPTER ONE

1.0       INTRODUCTION

1.1         Background to the Study

Statistical models utilize the information from independent variables to predict, understand relation or control a dependent variable. Regression analysis is one of the most widely used of all statistical methods for model building. Multiple regression models are models containing a number of predictor variables (Neter et al, 2005). The multiple linear regression models are used to study the relationship between a dependent variable and more than one independent variable (Greene, 2003). For instance, agriculture is an economic activity that is highly dependent upon weather or other climate variables in order to produce the food and fibre necessary to sustain human life. Not surprisingly, agriculture is deemed to be an economic activity that is expected to be vulnerable to climate variability and changes. One of the biggest long-term risks to global development is climate change. Choices and investment made in climate change mitigation and adaption are vital for ensuring sustainable and inclusive growth. Anon. (2014a). Any unfavorable climate will negatively affect agricultural growth (Murad et al, 2010). Therefore, climate change and climatic conditions phenomenon are important issues that should be taken into account in maintaining the sustainability and productivity of agricultural crops. There are various measures for crop cultivation which could be employed to adapt to the current climate change event in order to minimize crop damage in the event of unexpected bad weather (Adger et al, 2007). In order to identify how climate change and climate conditions could negatively impact the Nigeria, Malaysian and other nation’s socio-economy, it becomes necessary to understand the nature of climate variability. The description of the changing pattern of the climate could be understood by analyzing the pattern of daily temperature and….

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STUDY OF MULTICOLLINEARITY ON THE EFFECT OF CLIMATIC CONDITIONS ON OIL PALM YIELD AT NIFOR NIGERIA (STATISTICS PROJECT TOPICS AND MATERIALS)

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