# STEPWISE PROCEDURES IN DISCRIMINANT ANALYSIS

## The Complete Research Material is averagely 69 pages long and it is in Ms Word Format, it has 1-5 Chapters. Major Attributes are Abstract, All Chapters, Figures, Appendix, References. Study Level: BTech, BSc, BEng, BA, HND, ND or NCE. Full Access Fee: ₦7,000

Get the complete project »

Abstract

Several multivariate measurements require variables selection and ordering. Stepwise procedures ensure a step by step method through which these variables are selected and ordered usually for discrimination and classification purposes. Stepwise procedures in discriminant analysis show that only important variables are selected, while redundant variables (variables that contribute less in the presence of other variables) are discarded. The use of stepwise procedures is employed as to obtain a classification rule with a low error rate. Here in this work, variables are selected based on Wilks’ lambda Ù and partial F. The variable with the minimum Ù and maximum F is included in the model first, followed by the next most important variable as can be observed from the forward selection. Backward elimination deletes the variable with the smallest F and the largest Ù in a step by step fashion. SPSS is used to illustrate how stepwise procedures can be employed to identify the most important variable to be included in the model based on Wilks’ Ù and partial F. The analysis revealed that only variables X1, head width at the widest dimension and X4, eye-to-top-of-head measurement are the most important variables that are worthy of inclusion into the discriminant function.

CHAPTER ONE

INTRODUCTION

1.1    DISCRIMINANT ANALYSIS

Discriminant Analysis or D.A is a multivariate technique used to classify cases into distinct groups. It separates distinct sets of objects (or observations) and allocates new objects (or observations) to previously defined groups. Discriminant analysis is concerned with the problem of classification, which arises when a researcher having made a number of measurements on an individual, wishes to classify the individual into one of several categories on the basis of these multivariate measurements (Onyeagu, 2003).

Discriminant analysis will help us analyze the differences between groups and provide us with a means to assign or classify any case into the groups which it most closely resembles.

There are two aspects of discriminant analysis,

1.                 Predictive Discriminant Analysis (PDA) or Classification, which is concerned with classifying objects into one of several groups and

2.                 Descriptive Discriminant Analysis (DDA) which focused on revealing major differences among the groups (Stevens 1996).

2

According to Huberty (1994), Descriptive discriminant analysis includes the collection of techniques involving two or more criterion variables and a set of one or more grouping variables, each with two or more levels. “Whereas in predictive discriminant analysis (PDA) the multiple response variables play the role of predictor variables. In descriptive discriminant analysis (DDA) they are viewed as outcome variables and the grouping variable(s) as the explanatory variable(s). That is, the roles of the two types of variables involved in a multivariate multigroup setting in DDA are reversed from the role in PDA.

1.2    STEPWISE DISCRIMINANT ANALYSIS

A researcher may wish to discard variables that are redundant (in the presence of other variables) when a large number of variables are available for groups separation. Here (in discriminant analysis), variables (say y’s) are selected and, the basic model does not change. Unlike regression, where independent variables are selected and consequently, the model is altered.

Stepwise selection is a combination of forward and backward variables selection methods. In forward selection, the variable entered at

each step is the one that maximizes the partial F-Statistic based on Wilks’Ù. The maximal additional separation of groups above and beyond the

3

separation already attained by the other variables is thus obtained. The proportion of these F’s that exceed Fα is greater than α. While in backward

selection (elimination), the variable that contributes least is deleted at each step as shown by the partial F.

The variables which are selected one at a time, and at each step, are re-examined to see if any variable that entered earlier has become redundant in the presence of recently added variables. When the largest partial F among the variables available for entry fails to exceed a preset threshold value, the procedure stops.

Stepwise discriminant Analysis is a form of discriminant analysis. During the selection process no discriminant functions are calculated. However, after the completion of the subset selection, discriminant function is calculated for the selected variables. These variables can also be used in the construction of classification functions.

1.3    STEPS INVOLVED IN DISCRIMINANT ANALYSIS

1.                 Construct the discriminant function.

2.                 Evaluate the discriminant function for population one (1) by substituting the mean values of X1, X2, ….., Xp into Y = L1X1 + L2 X2+…+LP

### Quick Project Topic Search

• #### 1. TIME SERIES ANALYSIS ON RAINFALL PATTERN IN CALABAR MUNICIPALITY FROM 2002-2014 A CASE STUDY OF NIGERIAN METEOLOGICAL CENTRE CALABAR

» CHAPTER ONE INTRODUCTION 1.1. Background of the study Nigeria’s population and economic development are linked to climate sensitive activities i...Continue Reading »

Item Type & Format: Project Material - Ms Word |  57 pages |  Instant Download  |  Chapter 1-5  |  STATISTICS DEPARTMENT

• #### 2. APPLICATION OF QUEUEING THEORY IN TACKLING THE PROBLEM OF PORT CONGESTION AT APAPA PORT, LAGOS, NIGERIA

» Abstract The Apapa port is part of the ports operated by the Nigerian Ports Authority which was established in 1955 to oversee the activities and oper...Continue Reading »

Item Type & Format: Project Material - Ms Word |  68 pages |  Instant Download  |  Chapter 1-5  |  STATISTICS DEPARTMENT

• #### 3. STATISTICAL ANALYSIS ON INFLATION RATE IN NIGERIA (A CASE STUDY OF NATIONAL BUREAU OF STATISTICS 2010 - 2017)

» ABSTRACT This project work focuses on the analysis of Inflation Rate in Nigeria from 2010 – 2017. The data used is a secondary data collected fr...Continue Reading »

Item Type & Format: Project Material - Ms Word |  39 pages |  Instant Download  |  Chapter 1-5  |  STATISTICS DEPARTMENT

• #### 4. STATISTICAL MODELS OF DETERMINANTS OF BIRTH WEIGHT IN NKPOR AND ITS ENVIRONS, ANAMBRA STATE

» ABSTRACT Birth weight is an important indicator of child survival, future physical growth and mental development. Twenty million infants world wide re...Continue Reading »

Item Type & Format: Project Material - Ms Word |  131 pages |  Instant Download  |  Chapter 1-5  |  STATISTICS DEPARTMENT

• #### 5. DETERMINANTS OF ECONOMIC GROWTH IN NIGERIA: AN AUTOREGRESSIVE DISTRIBUTED LAG (ARDL) MODELING APPROACH

» CHAPTER ONE 1.1 Introduction Economically developed countries have been able to reduce their poverty level, strengthen their social and political inst...Continue Reading »

Item Type & Format: Project Material - Ms Word |  80 pages |  Instant Download  |  Chapter 1-5  |  STATISTICS DEPARTMENT

• #### 6. AN ASSESSMENT OF FERTILITY RATE AND DIFFERENTIALS IN WOMEN WITHIN THE REPRODUCTIVE AGEIN KADUNA STATE

Item Type & Format: Project Material - Ms Word |  88 pages |  Instant Download  |  Chapter 1-5  |  STATISTICS DEPARTMENT

• #### 7. TIME SERIES ANALYSIS ON MONTHLY SALES OF PETROLEUM PRODUCTS IN NIGERIA FOR 30 YEARS (A CASE STUDY OF NNPC, Osun State)

» CHAPTER ONE 1.0 INTRODUCTION 1.1 BACKGROUND OF STUDY The sales of petroleum products are the pivot behind the growth of the Nigeria economy. The Niger...Continue Reading »

Item Type & Format: Project Material - Ms Word |  81 pages |  Instant Download  |  Chapter 1-5  |  STATISTICS DEPARTMENT

• #### 8. BAYESIAN ESTIMATION OF THE SHAPE PARAMETER OF ODD GENERALIZED EXPONENTIAL-EXPONENTIAL DISTRIBUTION

» ABSTRACT The Odd Generalized Exponential Exponential Distribution (OGEED) could be used in various fields to model variables whose chances of success ...Continue Reading »

Item Type & Format: Project Material - Ms Word |  52 pages |  Instant Download  |  Chapter 1-5  |  STATISTICS DEPARTMENT

• #### 9. A STATISTICAL ANALYSIS OF ROAD ACCIDENTS IN NIGERIA (FEDERAL ROAD SAFETY CORPS ONITSHA, ANAMBRA STATE 2002-2015)

» CHAPTER ONE 1.1. INTRODUCTION Road transportation is by far the commonest means of transportation in Nigeria when compared to other means like air, ra...Continue Reading »

Item Type & Format: Project Material - Ms Word |  65 pages |  Instant Download  |  Chapter 1-5  |  STATISTICS DEPARTMENT

• #### 10. STATISTICAL ANALYSIS OF CRIMINAL OFFENCES RECORDED IN KUJE – ABUJA (FCT) FROM 1999 – 2007 (A CASE STUDY OF KUJE POLICE DIVISIONAL HEADQUARTERS,...

» CHAPTER ONE 1.1 INTRODUCTION The increasing desire of the government and civilization to improve the maintenance of law and order and to engage in cri...Continue Reading »

Item Type & Format: Project Material - Ms Word |  25 pages |  Instant Download  |  Chapter 1-5  |  STATISTICS DEPARTMENT