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Project Topic: PARTIAL LEAST SQUARES REGRESSION ESTIMATION OF NONORTHOGONAL PROBLEMS

  • Type: Project
  • Department: Statistic
  • Project ID: STS0081
  • Price: ₦3,000 ($20)
  • Pages: 64 Pages
  • Format: Microsoft Word
  • Views: 443

For more Info, call us on
+234 8130 686 500
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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   

PARTIAL LEAST SQUARES REGRESSION ESTIMATION OF NONORTHOGONAL PROBLEMS
For more Info, call us on
+234 8130 686 500
or
+234 8093 423 853

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  • Type: Project
  • Department: Statistic
  • Project ID: STS0081
  • Price: ₦3,000 ($20)
  • Pages: 64 Pages
  • Format: Microsoft Word
  • Views: 443
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    Details

    Type Project
    Department Statistic
    Project ID STS0081
    Price ₦3,000 ($20)
    No of Pages 64 Pages
    Format Microsoft Word

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