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Discriminant analysis
Discriminant analysis
Discriminant analysis
Discriminant analysis
Discriminant analysis
Discriminant analysis
Discriminant analysis
Discriminant analysis
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Discriminant analysis

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Discriminent

Discriminent

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  • 1 st table. A sample of 34 values wre taken and all of em were analyse 2 nd table Gives basic descriptive of each behaviour Valid n -- all 20 implies all 20 cases have been used.ie it gves number of variables used category wise.and no spoecific wwts have been assigned to any value 3 rd table Log determinant is for grup 0 and 1 for all the variables matyrix is drawn…and log determinant shud be higher 4 th table Sig value of F shud be less than 0.05 its d confidenc levels Eigen values shud be greater than 1 …for goodness of fit If EV >1for good discriminating power Wills lambdabetween 0 and 1…closer to 0 better it is Standardized function…relative importance of variables is loooked into..ie higher the value higher the importance Structure matrix and canonical are used in making overall function ie -6.058+0.032age+0.222income……………………… Grup centroid gives the avg of all d values of each grup Cut off score is weighted avg of grup centoid ie {20(1.914)-14(2.734)}/34 = 0.2 this is the ecentroid Nw if theD value is <0.2 then grup 0 ifD value>0.2 then grup 1 Clasification stats gives hw functions have been classified Squared mahalonobis distanceto centroid gives the values of all values of the distances of all variables from their own centroid..this distance is reported in further analysis df – degree of freedm
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    • 1. Discriminant Analysis DA is a technique for analyzing data when the criterion or dependent variable is categorical and the predictor or independent variables are interval in nature.
    • 2. Discriminant Analysis DA is sometimes also called Discriminant factor analysis Canonical discriminant analysis
    • 3. Objectives Development of discriminant functions Examination of whether significant differences exist among the groups, in terms of the predictor variables. Determination of which predictor variables contribute to most of the intergroup differences Evaluation of the accuracy of classification
    • 4. Multiple Discriminant Analyses Discriminant functions A function like multiple regression but different in the aspect that the dependent variable is categorical.
    • 5. Discriminant Analyses The discriminant score , or DA score, is the value resulting from applying a discriminant function formula to the data for a given case.
    • 6. Multiple Discriminant Analyses Discriminant coefficients are the regression- like b coefficients in the discriminant function, in the form D = b1x1 + b2x2 + ... + bnxn + c, where D is the latent variable formed by the discriminant function, the bs are discriminant coefficients, the xs are predictor variables, and c is a constant.
    • 7. Multiple Discriminant Analyses The structural discriminant function coefficients are partial coefficients, reflecting the unique contribution of each variable to the classification of the criterion variable The standardized discriminant coefficients , like beta weights in regression, are used to assess the relative classifying importance of the independent variables
    • 8. Discriminant AnalysesApplications:Market Research: Market segmentationFinancial Research: Default behaviorHuman resources: High performers

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