ABSTRACT
In this project, Partial Least Square Regression was compared with Ordinary Least Square Regression (OLSR) to handle the problem of multicollinarity and small sample size on all Nigeria Insurance Company’s expenditure data. The prediction methods have been compared for efficiency through Root Mean Square Error (RMSE) and Mean Square Error (MSE). It is found that in this project Partial Least Square Regression (PLSR) provides better prediction as compared to the Ordinary Least Square Regression (OLSR).
TABLE OF CONTENTS
Title page
Declaration
Certification
Dedication
Acknowledgement
Abstract
Table of Contents
CHAPTER ONE
INTRODUCTION
1.1 Background of Study
1.2 Statement of Problem
1.3 Justification for the Study
1.4 Scope of the Study
1.5 Aim and Objectives
1.6 Limitation of the Study
1.7 Definition of terms
1.8 Outline of study
CHAPTER TWO
2.1 Literature Review
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Ordinary Least Square Regression
3.2 Assumptions of Multiple Regression
3.3 Partial Least Squares for Nonorthogonal Problem
3.3.1 General Form of Partial Least Square
3.3.2 Assumptions Underlying Partial Least Square Regression
3.3.3 The Main Analytical Tool
3.4 Correlation Matrix
3.5 The Variance Inflation factor
3.6 Tolerance Factor
3.7 Coefficient of Determination
3.8 Adjusted R
3.9 Definition of Durbin Watson’s Statistic
3.10 Root Mean Square Deviation
3.11 ANOVA for Multiple Regression
3.12 Confidence Intervals for Multiple Regression
3.13 Grubbs Test for Outliers
3.14 Test on Individual Regression Coefficients
3.15 Statistic
3.16 Q-Q Plot
3.17 White’s Test for Heteroscedasticity
3.18 Data Presentation
CHAPTER FOUR
DATA ANALYSIS AND INTERPRETATIONS
4.1 Ordinary Least Squares Regression Results
4.1.2 Summary Statistics
4.1.3 Correlation Matrix
4.1.4 White’s Test of Heteroscedasticity
4.1.5 Grubb’s Test
4.1.6 Multicollinearity Statistics
4.1.7 Goodness of Fit Statistics
4.1.8 Analysis of Variance
4.1.9 Model Parameter
4.1.10 O.L.S.R Predictions and Residuals
4.2 Partial Least Square Regression
CHAPTER FIVE
5.1 Summary
5.2 Conclusion
References
