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Stepwise Procedures In Discriminant Analysis
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5 is called the mahalanobis (squared) distance for known parameters.
For unknown parameters, the Mahalanobis (squared) distance is obtained by
estimating p1, p2 and S by X1, X2 and S, respectively.
Following the same technique the Mahalanobis (Squared) distance,
D , for the unknown parameters is D2 = (X- X)+S-1 (X1- X2) .
The distribution of D can be used to test if there are significant differences between the two groups.
2.4 WELCH’S CRITERION
Welch (1939) suggested that minimizing the total probability of misclassification would be a sensible idea.
The discriminant function to be calculated from the observed measurements is the ratio of the probability laws. The criterion level to which the discriminant function is to be referred depends on the prior probabilities.
Von Mises (1945) suggested minimizing the maximum probability of misclassification in the two groups.
2.4.2 UNEQUAL COST OF MISCLASSIFICATION CRITERION
Since different types of misclassification have different costs, various authors have advocated minimizing the total cost of misclassification.
Let the cost of misclassifying a member of population i, A i = Ci , and the cost of misclassifying a member of population 2, A2 = C2 .
Then we wish to find two regions Ri and R2 to minimize
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ABSRACT - [ Total Page(s): 1 ]
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 ... Continue reading---
APPENDIX A - [ Total Page(s): 1 ] ... Continue reading---
APPENDIX B - [ Total Page(s): 1 ] APPENDIX II BACKWARD ELIMINATION METHOD The procedure for the backward elimination of variables starts with all the x’s included in the model and deletes one at a time using a partial  or F. At the first step, the partial  for each xi isThe variable with the smallest F or the largest  is deleted. At the second step of backward elimination of variables, a partial  or F is calculated for each q-1 remaining variables and again, the variable which is th ... Continue reading---
TABLE OF CONTENTS - [ Total Page(s): 1 ]TABLE OF CONTENTSPageTitle PageApproval pageDedicationAcknowledgementAbstractTable of ContentsCHAPTER 1: INTRODUCTION1.1 Discriminant Analysis1.2 Stepwise Discriminant analysis1.3 Steps Involved in discriminant Analysis1.4 Goals for Discriminant Analysis1.5 Examples of Discriminant analysis problems1.6 Aims and Obj ectives1.7 Definition of Terms1.7.1 Discriminant function1.7.2 The eigenvalue1.7.3 Discriminant Score1.7.4 Cut off1.7 ... Continue reading---
CHAPTER ONE - [ Total Page(s): 2 ] DEFINITION OF TERMS Discriminant Function This is a latent variable which is created as a linear combination of discriminating variables, such that Y = L1X1 + L2X2 + + Lp Xp where the L’s are the discriminant coefficients, the x’s are the discriminating variables. The eigenvalue: This is the ratio of importance of the dimensions which classifies cases of the dependent variables. There is one eigenvalue for each discriminant functio ... Continue reading---
CHAPTER THREE - [ Total Page(s): 5 ]The addition of variables reduces the power of Wilks’ Λ test statistics except if the added variables contribute to the rejection of Ho by causing a significant decrease in Wilks’ Λ ... Continue reading---
CHAPTER FOUR - [ Total Page(s): 3 ]CHAPTER FOUR DATA ANALYSISMETHOD OF DATA COLLECTIONThe data employed in this work are as collected by G.R. Bryce andR.M. Barker of Brigham Young University as part of a preliminary study of a possible link between football helmet design and neck injuries.Five head measurements were made on each subject, about 30 subjects per group:Group 1 = High School Football players Group 2 = Non-football playersThe five variables areWDIM = X1 = head width at wi ... Continue reading---
CHAPTER FIVE - [ Total Page(s): 1 ]CHAPTER FIVERESULTS, CONCLUSION AND RECOMMENDATIONRESULTSAs can be observed from the results of the analysis, when discriminant analysis was employed, the variable CIRCUM(X2) has the highest Wilks’ lambda of 0.999 followed by FBEYE (X2) (0.959). The variable EYEHD (X4) has the least Wilks’ lambda of 0.517 followed by EARHD (X5) (0.705). Also the least F-value was recorded with the variable CIRCUM (X2) (0.074) followed by the variable FBEYE (X2) (2.474), while the variable EYEHD (X4 ... Continue reading---
REFRENCES - [ Total Page(s): 1 ] REFERENCES Anderson, T.W. (1958). An introduction to multivariate statistical Analysis. John Wiley & Sons Inc., New York. Cohen, J. (1968). Multiple regression as a general data-analytic system. Psychological Bulletin 70, 426-443. Cooley W.W. and Lohnes P.R. (1962). Multivariate procedures for the Behavioural Sciences, New York John Wiley and Sons Inc. Efroymson, M.A. (1960). Multiple regression analysis. In A. Raston & H.S. Wilfs (Eds.) Mathematical methods for ... Continue reading---
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ABSRACT - [ Total Page(s): 1 ]
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 ... Continue reading---