PRINCIPAL COMPONENT ANALYSIS OF STUDENTS PERFORMANCE (A CASE STUDY OF BAPTIST SECONDARY SCHOOL ONITSHA, ANAMBRA STATE).

ABSTRACT

Often in multivariate analysis, fairly large numbers are be used but the objectives of principal component analysis are data reduction and data interpretation.

Principal component analysis can reveal relationships that were not ordinarily result.

 Principle component analysis as reduction technique to reduce the variable beings considered in the performance of students in Anambra State and to know many variables to keep and how many to discard. The use of correlation matrix in the data analysis which takes care of the correlation of the principal components not being invariant under separate scaling of the original set of variable. In this work principle component analyses data form the past two WAEC result 2007 and 2008 sheet contain the entire senior secondary school three that took part in (8) different courses. In analyzing the data, statistic package known as state view and SPSS where used.

In conclusion it has been established that four linear combination for the first set of data can be replace by the original eight  variables without loss of information.   

TABLE OF CONTENTS

Title page                                                                                         i

Approval page                                                                                 ii

Dedication                                                                                       iii

Acknowledgement                                                                          iv

Abstract                                                                                           v

Table of contents                                                                            vi

Chapter one    ---------- Introduction

1. 0   Introduction                                                                            1

1.1    What is principal component analysis                                3

1.2    Importance of principal component analysis                     5

1.3       Aims And Objectives                                                            6

Chapter two ---------------     Literature review 

Literature review                                                                             7

2.0 Introduction                                                                               7

Chapter three -----------Data collection

3.0 Data collection and methodology                                           9

3.1 Description of data                                                                            19

     Appendix I                                                                                   21                                 

   Appendix II                                                                                   24

Chapter four  ---------------Analysis of data     

4.0    Analysis of data                                                                   27

4.1      Interpretation of principal component result of the analysis of m1  

                                                                                                          35                                                                                                                                   

Chapter five ----Summary, Conclusion & Recommendation

5.0    Summary                                                                               39

5.1    Conclusion                                                                            39

5.2    Recommendation                                                                 39

References                                                                                     40

CHAPTER ONE

INTRODUCTION

1. 0   INTRODUCTION                           

When the casual relationship between dependent variable and independent variable have to be explained and interpreted an x variable (independent variables) are highly correlated, multiple regression analysis becomes unsatisfactory.

Principal component analysis (P C A) is concerned with explaining the variance – covariance structure through a few linear combinations of the original variable with it general objective in data education and interpretation.

The general objective (according to S.I. Onyeagu) principal component analysis are data reduction and data interpretation techniques. A principal component analysis can reveal relationship that were not previous suspected thereby allowing interpretations that would not ordinarily result.

Principal component analysis is an advanced method and techniques by which a set of observed x variable can be expressed or transformed as a linear combination of smaller set of principal component which are linear independent.

Although principal component are required to reproduce the total system variability, often much of this variability can be counted by smaller number k of principal component. If so, there is almost as much information in the k components as there is in the original p variables the k principal components are more of a means   because they frequently serve as intermediate steps in much larger investigations. Principal components often reveal relationship that were not previously suspected and there by allows interpretations that would not ordinary result.

Principal component analysis examine whether the joint variable in p observable random variable x₁,x₂…..x_p can describe approximately in terms of the joint variation of a fewer number, say K < P of hypothetical  variable. In other words, we want to replace a set of P variable by K linear function, K < P, without much loss of information. To do this we usually seek for linear transportation of this type y_i = ∑a і = I, 2 ….p which we describe the original variable in lesser number of uncorrected dimensions. This is accomplished by the analysis of all the correlations among the variables. The success of the method depends on obtained two or three new uncorrelated variables, which account for as much of the variation as possible. If the first two or three of these new variables account for nearly the whole of the variation and the contribution of the other p² a p^s  is small, we may say that the total variation is approximately accounted for the first two or three of the new variables, and we may therefore neglect the remainder suppose, λ₁, λ₂,…._. λ_m    be characteristic root of A we can find a vector pi (m x_i), such that A PI = x_ip_i, the p is called the latent  the vectors. Given the latent root  λ_{1 ≥} λ_{2 ≥}  …_≥λ_m and the corresponding latent vectors pi,

P_2,…..p_m, the linear function

Y₁ = pix corresponding to  λ₁

y_{2 = P2x}     corresponding to λ₂

- - - - - - - - - - - - - - - - - - -

- - - - - - - - - - - - - - - - - - - -

- - - - - - - - - - - - - - - - - - - -           

y_{m  =  p1mx} corresponding to λ_m

is called the principal component of x.

1.1    WHAT IS PRINCIPAL COMPONENT ANALYSIS

A principal component analysis is concern with explaining it’s method and technique by which a set of observe variables can be expressed or transformed as linear combination of smaller set of principal component which are linearly independent in other words, principal component analysis which aims to resolve the total variation of a set of variable into linear independent variability in data.

          Principal component is also concerned with explaining the variance – covariance structure through a few linear combination of the original variables. Its general objectives are.

1.           Data reduction and

2.           Interpretation.

The basic idea in carrying out principal component analysis is that the back of observation will be very close to a linear sub-space and hence one can use a new coordinates along the data explain great variability.

Most time we are dealing with a falsely large number p^k of correlated random variable, it would be useful if we could reduce it to a smaller number of random variable in such away that

i         The random number of variable account for the large parts of the total variability.

ii        The remaining number of variable are interpretable in terms of original problem.

          In conclusion, a lot of useful information can be deprived and advice given using principal component analysis on normal data, provided the multivariate data have good structure to be extracted from it. In this word, the variables are assumed to have multivariate normal distribution.

1.2    IMPORTANCE OF PRINCIPAL COMPONENT ANALYSIS

Some of the importances are:

1.   An analysis of principal components are more of means to an end rather than an end in themselves because they frequently serve as intermediate step in much large investigations.

2.   Principal component analysis may be a solution of inputs to multiple regressions.

3.   An analysis of principal component often reveals relationship that not previously suspected and thereby allows interpretations that would not ordinarily result.

4.   Principal component analysis provides a statically method for detecting and interpreting linear singular reties in a set of data.

5.   Principal  components analysis are one factoring of the covariance matrices for the factor analysis model    

1.4       AIMS AND OBJECTIVES

Aims and objective of this work include. 

1.           To determine the relative correlation between the various subjects.

2.           To determine the relatives contribution of each course to the performance of students.

3.           To investigate the effect of some subject over the other.

4.           To show principal component potentials as a   means to explain the performance of student on some course.

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