STATISTICAL ANALYSIS ON EDUCATION TRUST FUND ALLOCATION TO TERITIARY INSTITUTION IN SIX GEOPOLITICAL ZONES OF NIGERIA

Description

ABSTRACT

In this project entitled statistical analysis on education trust fund allocation to tertiary institutions in six geopolitical zones of Nigeria, the average allocation to zones, method of distributions, extraction of principal components, classification of the components into factors and to test if there is any significant difference in the allocation among the zones was carried out using principal components analysis, factor analysis, normality test just to mention but a few. The average allocation to all the zones within the period under review was 14,605,429,76. The allocation to zones was normally distributed indicating unbiasedness in the allocations. University allocation is the principal factor component in the ETF allocation among the institutions revealing high contribution of university with 0.201 in the first component, followed by monotechnics, polytechnics and colleges of education. With little difference in the allocations among polytechnics, monotechnics and colleges of education, they were grouped into one factor and university in another factor. Based on the results obtained; no zone is more favored and their distribution is unbiased

TABLE OF CONTENT

TITLE PAGE

CERTIFICATION

ACKNOWLEGMENT ..iii

DEDICATION iv

ABSTRACT .v

TABLE OF CONTENT ..vi

CHAPTER ONE

1.0 INTRODUCTION .1

1.1 BACKGROUND OF STUDY .1

1.2 SOME FACTS ABOUT NIGERIA EDUCATION 4

1.3 STATEMENT OF PROBLEMS .12

1.4 PURPOSE OF THE STUDY ..12

1.5 SIGNIFICANCE OF THE STUDY .12

1.6 SCOPE OF THE STUDY 13

1.7 AIMS AND OBJECTIVES 13

1.8 TEST OF HYPOTHESIS .14

1.9 OPERATION KEY WORDS . 14

1.10 ABBREVIATIONS 15

CHAPTER TWO LITRATURE REVIEW

2.0 INTRODUCTION ..16

CHAPTER THREE METHODOLOGY

3.0 INTRODUCTION .27

3.1 THE SAMPLED POPULATION 27

3.2 METHODS OF DATA COLLECTION .28

3.3 PRINCIPAL COMPONENT ANALYSIS ..28

3.4 FACTOR ANALYSIS 30

3.5 KRUSKALWALLIS TEST ..34

CHAPTER FOUR

4.0 ANALYSIS OF DATA ..36

4.1 TO KNOW THE DISTRIBUTION OF ETF ALLOCATION

TO TETIARY INSTITUTIONS IN NIGERIA 36

4.2 KRUSKALWALLIS TEST ..37

4.3 PRINCIPALCOMPONENTANALYSIS..39

4.4 TIME SERIES.42

4.5 FACTOR ANALYSIS ..43

CHAPTER FIVE

5.0 SUMMARY ..49

5.1 CONCLUSION .51

5.2 RECOMMENDATION 52

REFERENCES 54

APPENDIX 56

CHAPTER ONE

1.0 INTRODUCTION

1.1 BACKGROUND OF STUDY

In Principal Components Analysis PCA and Factor Analysis FA one wishes to extract from a set of P variables a reduced set of M components or factors that accounts for most of the variance in a P variables in other words, we wish to reduce a set of P variables to a set of M underlying super ordinate dimensions.

These underlying factors are inferred from the correlations among the P variables. Each factor is estimated as a weighted sum of the P variables. The factor is thus;

F1 W1X1 Wi2X2 W1pXp K.

One may also express each of the P variables as a linear combination of the M factors,

Xj Aij F1 A2j F2 Amj Fm k Uj

Where Uj is the variance that is unique to variable j, variance that cannot be explained by any of the common factors.

Principal component analysis is a variable reduction procedure which provides guidelines regarding the necessary sample size and number of items per component.