Partial Least Squares Regression Estimation of Nonorthogonal problems

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).

Title page

Declaration

Certification

Dedication

Acknowledgement

Abstract

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

3.9 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

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APA

BARTHOLOMEW, D. (2018). Partial Least Squares Regression Estimation of Nonorthogonal problems. Afribary. Retrieved from https://afribary.com/works/partial-least-squares-regression-estimation-of-nonorthogonal-problems-5261

MLA 8th

BARTHOLOMEW, DESMOND "Partial Least Squares Regression Estimation of Nonorthogonal problems" Afribary. Afribary, 29 Jan. 2018, https://afribary.com/works/partial-least-squares-regression-estimation-of-nonorthogonal-problems-5261. Accessed 23 Jul. 2024.

MLA7

BARTHOLOMEW, DESMOND . "Partial Least Squares Regression Estimation of Nonorthogonal problems". Afribary, Afribary, 29 Jan. 2018. Web. 23 Jul. 2024. < https://afribary.com/works/partial-least-squares-regression-estimation-of-nonorthogonal-problems-5261 >.

Chicago

BARTHOLOMEW, DESMOND . "Partial Least Squares Regression Estimation of Nonorthogonal problems" Afribary (2018). Accessed July 23, 2024. https://afribary.com/works/partial-least-squares-regression-estimation-of-nonorthogonal-problems-5261

Document Details
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