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.
TABLE OF CONTENTS
TITLE PAGE
TABLE OF CONTENTS
LIST OF FIGURES
LIST OF TABLES
LIST OF ABBREVIATIONS/NOTATIONS
ABSTRACT
CHAPTER ONE
1.0 INTRODUCTION
1.1 Background to the Study
1.2 Statement of Research Problem
1.3 Scope and Limitation of the Research
1.4 Relevance and Significance of the Study
1.5 Justification
1.6 Aim and Objectives of the Study
1.7 Definition of Terms used in the Study
CHAPTER TWO
2.0 REVIEW OF LITERATURE
2.1 Multicolinearity
2.2 The Concept of Climate, Climate Change and Climate Conditions
2.3 Climate Change / Climatic conditions in some Specific Places
2.4 The Effect of Climate Change / Climate Conditions in Agriculture
2.5 The Impact of Climate Change / Climate Conditions in Oil Palm Yield
2.6 Vulnerability and Adaptation of Oil Palm to Climate Change and Climate Conditions
2.7 Modelling the Effect of Climate Change and Climate Conditions on Oil Palm Yield
CHAPTER THREE
3.0 MATERIALS AND METHOD
3.1 The Method of Data Collection
3.2 Methods of Detecting Multicollinearity
3.2.1 Kleins Multicollinearity Test
3.2.2 Frisch’s Confluence Analysis Multicollinearity Test
3.2.3 Eigenvalues & Condition Index Multicollinearity Test
3.2.4 Bunch-Map Analysis Multicollinearity Test
3.2.5 Farrar-Glauber Multicollinearity Test
3.2.6 Variance Inflation Factor (VIF)
3.3 Alternative Methods for Detecting Multicollinearity
3.3.1 High R 2 but Few Significant t-Ratio
3.3.2 High Pair-Wise Correlations among Independent variables
3.3.3 Examination of Partial Correlations
3.3.4 Auxiliary Regression
3.3.5 Comparison of F and T test
3.4 Review of Related and Relevant Statistical Methods to Multicollinearity
3.5 Classical Multiple Linear Regression
3.6 Relating the Model to oil Palm Yield and Climate Variables
3.7 Stepwise Regression
3.8 Principal Component Regression (PCR)
3.9 The Ordinary Ridge Regression (ORR)
3.10 Method of Estimating K
3.11 Performance Measure
CHAPTER FOUR
4.0 RESULTS AND DISCUSSION
4.1 Introduction
4.2 Data Analysis
4.3 Multicollinearity Diagnostics
4.4 Counteracting Multcollinearity
4.5 Comparison on Stepwise Regression (STEP), Ridge Regression (RR), and Principal Component Regression (PCR)
4.6 Discussion of Result or Findings
CHAPTER FIVE
5.0 SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary
5.2 Conclusion
5.3 Recommendations
5.4 Suggestion for Further Study
REFERENCES
APPENDICES
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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Item Type: Project Material | Size: 89 pages | Chapters: 1-5
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