1. Copyright © 2010 Pearson Education, Inc. 18-1 Chapter Eighteen Discriminant and Logit Analysis

2. Copyright © 2010 Pearson Education, Inc. 18-2 Chapter Outline 1) Overview 2) Basic Concept 3) Relation to Regression and ANOVA 4) Discriminant Analysis Model 5) Statistics Associated with Discriminant Analysis 6) Conducting Discriminant Analysis i. Formulation ii. Estimation iii. Determination of Significance iv. Interpretation v. Validation

3. Copyright © 2010 Pearson Education, Inc. 18-3 Chapter Outline 7)Multiple Discriminant Analysis i.Formulation ii.Estimation iii.Determination of Significance iv.Interpretation v.Validation 8)Stepwise Discriminant Analysis

4. Copyright © 2010 Pearson Education, Inc. 18-4 Chapter Outline 9) The Logit Model i.Estimation ii.Model Fit iii.Significance Testing iv.Interpretation of Coefficients v.An Illustrative Application 10) Summary

5. Copyright © 2010 Pearson Education, Inc. 18-5 Similarities and Differences between ANOVA, Regression, and Discriminant Analysis Table 18.1 ANOVA REGRESSION DISCRIMINANT/LOGIT Similarities Number ofOneOneOne dependent variables Number of independentMultipleMultipleMultiple variables Differences Nature of the dependentMetricMetricCategorical variables Nature of the independentCategoricalMetricMetric variables

6. Copyright © 2010 Pearson Education, Inc. 18-6 Discriminant Analysis Discriminant analysis is a technique for analyzing data when the criterion or dependent variable is categorical and the predictor or independent variables are interval in nature. The objectives of discriminant analysis are as follows: Development of discriminant functions, or linear combinations of the predictor or independent variables, which will best discriminate between the categories of the criterion or dependent variable (groups). 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. Classification of cases to one of the groups based on the values of the predictor variables. Evaluation of the accuracy of classification.

7. Copyright © 2010 Pearson Education, Inc. 18-7 Discriminant Analysis When the criterion variable has two categories, the technique is known as two-group discriminant analysis. When three or more categories are involved, the technique is referred to as multiple discriminant analysis. The main distinction is that, in the two-group case, it is possible to derive only one discriminant function. In multiple discriminant analysis, more than one function may be computed. In general, with G groups and k predictors, it is possible to estimate up to the smaller of G - 1, or k, discriminant functions. The first function has the highest ratio of between-groups to within-groups sum of squares. The second function, uncorrelated with the first, has the second highest ratio, and so on. However, not all the functions may be statistically significant.

8. Copyright © 2010 Pearson Education, Inc. 18-8 Geometric Interpretation Fig. 18.1 X 2 X 1 G1 G2 D G1 G2 2 2 2 2 2 2 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1

9. Copyright © 2010 Pearson Education, Inc. 18-9 Discriminant Analysis Model The discriminant analysis model involves linear combinations of the following form: D = b 0 + b 1 X 1 + b 2 X 2 + b 3 X 3 +... + b k X k Where: D=discriminant score b 's=discriminant coefficient or weight X 's=predictor or independent variable The coefficients, or weights (b), are estimated so that the groups differ as much as possible on the values of the discriminant function. This occurs when the ratio of between-group sum of squares to within-group sum of squares for the discriminant scores is at a maximum.

10. Copyright © 2010 Pearson Education, Inc. 18-10 Statistics Associated with Discriminant Analysis Canonical correlation. Canonical correlation measures the extent of association between the discriminant scores and the groups. It is a measure of association between the single discriminant function and the set of dummy variables that define the group membership. Centroid. The centroid is the mean values for the discriminant scores for a particular group. There are as many centroids as there are groups, as there is one for each group. The means for a group on all the functions are the group centroids. Classification matrix. Sometimes also called confusion or prediction matrix, the classification matrix contains the number of correctly classified and misclassified cases.

11. Copyright © 2010 Pearson Education, Inc. 18-11 Statistics Associated with Discriminant Analysis Discriminant function coefficients. The discriminant function coefficients (unstandardized) are the multipliers of variables, when the variables are in the original units of measurement. Discriminant scores. The unstandardized coefficients are multiplied by the values of the variables. These products are summed and added to the constant term to obtain the discriminant scores. Eigenvalue. For each discriminant function, the Eigenvalue is the ratio of between-group to within-group sums of squares. Large Eigenvalues imply superior functions.

12. Copyright © 2010 Pearson Education, Inc. 18-12 Statistics Associated with Discriminant Analysis F values and their significance. These are calculated from a one-way ANOVA, with the grouping variable serving as the categorical independent variable. Each predictor, in turn, serves as the metric dependent variable in the ANOVA. Group means and group standard deviations. These are computed for each predictor for each group. Pooled within-group correlation matrix. The pooled within-group correlation matrix is computed by averaging the separate covariance matrices for all the groups.

13. Copyright © 2010 Pearson Education, Inc. 18-13 Statistics Associated with Discriminant Analysis Standardized discriminant function coefficients. The standardized discriminant function coefficients are the discriminant function coefficients and are used as the multipliers when the variables have been standardized to a mean of 0 and a variance of 1. Structure correlations. Also referred to as discriminant loadings, the structure correlations represent the simple correlations between the predictors and the discriminant function. Total correlation matrix. If the cases are treated as if they were from a single sample and the correlations computed, a total correlation matrix is obtained. Wilks'. Sometimes also called the U statistic, Wilks' for each predictor is the ratio of the within-group sum of squares to the total sum of squares. Its value varies between 0 and 1. Large values of (near 1) indicate that group means do not seem to be different. Small values of (near 0) indicate that the group means seem to be different.

14. Copyright © 2010 Pearson Education, Inc. 18-14 Conducting Discriminant Analysis Fig. 18.2 Assess Validity of Discriminant Analysis Estimate the Discriminant Function Coefficients Determine the Significance of the Discriminant FunctionFormulate the Problem Interpret the Results

15. Copyright © 2010 Pearson Education, Inc. 18-15 Conducting Discriminant Analysis Formulate the Problem Identify the objectives, the criterion variable, and the independent variables. The criterion variable must consist of two or more mutually exclusive and collectively exhaustive categories. The predictor variables should be selected based on a theoretical model or previous research, or the experience of the researcher. One part of the sample, called the estimation or analysis sample, is used for estimation of the discriminant function. The other part, called the holdout or validation sample, is reserved for validating the discriminant function. Often the distribution of the number of cases in the analysis and validation samples follows the distribution in the total sample.

16. Copyright © 2010 Pearson Education, Inc. 18-16 Information on Resort Visits: Analysis Sample Table 18.2 Annual Attitude Importance Household Age of Amount Resort Family Toward Attached Size Head of Spent on No. Visit Income Travel to Family Household Family ($000) Vacation Vacation 1150.2 5 8 3 43 M (2) 2170.3 6 7 4 61 H (3) 3162.9 7 5 6 52 H (3) 4148.5 7 5 5 36 L (1) 5152.7 6 6 4 55 H (3) 6175.0 8 7 5 68 H (3) 7146.2 5 3 3 62 M (2) 8157.0 2 4 6 51 M (2) 9164.1 7 5 4 57 H (3) 10168.1 7 6 5 45 H (3) 11173.4 6 7 5 44 H (3) 12171.9 5 8 4 64 H (3) 13156.2 1 8 6 54 M (2) 14149.3 4 2 3 56 H (3) 15162.0 5 6 2 58 H (3)

17. Copyright © 2010 Pearson Education, Inc. 18-17 Information on Resort Visits: Analysis Sample Table 18.2, cont. Annual Attitude Importance Household Age of Amount Resort Family Toward Attached Size Head of Spent on No. Visit Income Travel to Family Household Family ($000) Vacation Vacation 16232.1 5 4 3 58 L (1) 17236.2 4 3 2 55 L (1) 18243.2 2 5 2 57 M (2) 19250.4 5 2 4 37 M (2) 20244.1 6 6 3 42 M (2) 21238.3 6 6 2 45 L (1) 22255.0 1 2 2 57 M (2) 23246.1 3 5 3 51 L (1) 24235.0 6 4 5 64 L (1) 25237.3 2 7 4 54 L (1) 26241.8 5 1 3 56 M (2) 27257.0 8 3 2 36 M (2) 28233.4 6 8 2 50 L (1) 29237.5 3 2 3 48 L (1) 30241.3 3 3 2 42 L (1)

18. Copyright © 2010 Pearson Education, Inc. 18-18 Information on Resort Visits: Holdout Sample Table 18.3 Annual Attitude Importance Household Age of Amount Resort Family Toward Attached Size Head of Spent on No. Visit Income Travel to Family Household Family ($000) Vacation Vacation 1150.8 4 7 3 45 M(2) 2163.6 7 4 7 55 H (3) 3154.0 6 7 4 58 M(2) 4145.0 5 4 3 60 M(2) 5168.0 6 6 6 46 H (3) 6162.1 5 6 3 56 H (3) 7235.0 4 3 4 54 L (1) 8249.6 5 3 5 39 L (1) 9239.4 6 5 3 44 H (3) 10237.0 2 6 5 51 L (1) 11254.5 7 3 3 37 M(2) 12238.2 2 2 3 49 L (1)

19. Copyright © 2010 Pearson Education, Inc. 18-19 Conducting Discriminant Analysis Estimate the Discriminant Function Coefficients The direct method involves estimating the discriminant function so that all the predictors are included simultaneously. In stepwise discriminant analysis, the predictor variables are entered sequentially, based on their ability to discriminate among groups.

20. Copyright © 2010 Pearson Education, Inc. 18-20 Results of Two-Group Discriminant Analysis Table 18.4 GROUP MEANS VISITINCOME TRAVEL VACATION HSIZE AGE 160.52000 5.40000 5.80000 4.33333 53.73333 241.91333 4.33333 4.06667 2.80000 50.13333 Total51.21667 4.86667 4.9333 3.56667 51.93333 Group Standard Deviations 1 9.83065 1.91982 1.82052 1.23443 8.77062 2 7.55115 1.95180 2.05171.94112 8.27101 Total 12.79523 1.97804 2.09981 1.33089 8.57395 Pooled Within-Groups Correlation Matrix INCOMETRAVELVACATION HSIZE AGE INCOME 1.00000 TRAVEL0.19745 1.00000 VACATION0.091480.08434 1.00000 HSIZE0.08887-0.01681 0.07046 1.00000 AGE - 0.01431-0.19709 0.01742 -0.04301 1.00000 Wilks' (U-statistic) and univariate F ratio with 1 and 28 degrees of freedom VariableWilks' F Significance INCOME0.45310 33.800 0.0000 TRAVEL0.92479 2.277 0.1425 VACATION0.82377 5.990 0.0209 HSIZE0.65672 14.640 0.0007 AGE0.95441 1.338 0.2572 Cont.

21. Copyright © 2010 Pearson Education, Inc. 18-21 Results of Two-Group Discriminant Analysis Table 18.4, cont. CANONICAL DISCRIMINANT FUNCTIONS % of Cum Canonical After Wilks' FunctionEigenvalue Variance %Correlation Function Chi-square df Significance : 0 0.3589 26.130 50.0001 1* 1.7862 100.00 100.00 0.8007 : * marks the 1 canonical discriminant functions remaining in the analysis. Standard Canonical Discriminant Function Coefficients FUNC 1 INCOME0.74301 TRAVEL0.09611 VACATION0.23329 HSIZE0.46911 AGE0.20922 Structure Matrix: Pooled within-groups correlations between discriminating variables & canonical discriminant functions (variables ordered by size of correlation within function) FUNC 1 INCOME0.82202 HSIZE0.54096 VACATION0.34607 TRAVEL0.21337 AGE0.16354 Cont.

22. Copyright © 2010 Pearson Education, Inc. 18-22 Cont. Results of Two-Group Discriminant Analysis Table 18.4, cont. Unstandardized Canonical Discriminant Function Coefficients FUNC 1 INCOME0.8476710E-01 TRAVEL0.4964455E-01 VACATION0.1202813 HSIZE0.4273893 AGE0.2454380E-01 (constant)-7.975476 Canonical discriminant functions evaluated at group means (group centroids) GroupFUNC 1 1 1.29118 2-1.29118 Classification results for cases selected for use in analysis PredictedGroup Membership Actual GroupNo. of Cases12 Group 1 15123 80.0%20.0% Group 2 15015 0.0%100.0% Percent of grouped cases correctly classified: 90.00%

23. Copyright © 2010 Pearson Education, Inc. 18-23 Results of Two-Group Discriminant Analysis Table 18.4, cont. Classification Results for cases not selected for use in the analysis (holdout sample) PredictedGroup Membership Actual GroupNo. of Cases12 Group 1642 66.7%33.3% Group 2606 0.0%100.0% Percent of grouped cases correctly classified: 83.33%.

24. Copyright © 2010 Pearson Education, Inc. 18-24 Conducting Discriminant Analysis Determine the Significance of Discriminant Function The null hypothesis that, in the population, the means of all discriminant functions in all groups are equal can be statistically tested. In SPSS this test is based on Wilks'. If several functions are tested simultaneously (as in the case of multiple discriminant analysis), the Wilks' statistic is the product of the univariate for each function. The significance level is estimated based on a chi-square transformation of the statistic. If the null hypothesis is rejected, indicating significant discrimination, one can proceed to interpret the results.

25. Copyright © 2010 Pearson Education, Inc. 18-25 Conducting Discriminant Analysis Interpret the Results The interpretation of the discriminant weights, or coefficients, is similar to that in multiple regression analysis. Given the multicollinearity in the predictor variables, there is no unambiguous measure of the relative importance of the predictors in discriminating between the groups. With this caveat in mind, we can obtain some idea of the relative importance of the variables by examining the absolute magnitude of the standardized discriminant function coefficients. Some idea of the relative importance of the predictors can also be obtained by examining the structure correlations, also called canonical loadings or discriminant loadings. These simple correlations between each predictor and the discriminant function represent the variance that the predictor shares with the function. Another aid to interpreting discriminant analysis results is to develop a Characteristic profile for each group by describing each group in terms of the group means for the predictor variables.

26. Copyright © 2010 Pearson Education, Inc. 18-26 Conducting Discriminant Analysis Assess Validity of Discriminant Analysis Many computer programs, such as SPSS, offer a leave-one- out cross-validation option. The discriminant weights, estimated by using the analysis sample, are multiplied by the values of the predictor variables in the holdout sample to generate discriminant scores for the cases in the holdout sample. The cases are then assigned to groups based on their discriminant scores and an appropriate decision rule. The hit ratio, or the percentage of cases correctly classified, can then be determined by summing the diagonal elements and dividing by the total number of cases. It is helpful to compare the percentage of cases correctly classified by discriminant analysis to the percentage that would be obtained by chance. Classification accuracy achieved by discriminant analysis should be at least 25% greater than that obtained by chance.

27. Copyright © 2010 Pearson Education, Inc. 18-27 Results of Three-Group Discriminant Analysis Table 18.5 Group Means AMOUNTINCOMETRAVELVACATIONHSIZEAGE 138.570004.500004.70000 3.1000050.30000 250.110004.000004.200003.40000 49.50000 364.970006.100005.90000 4.20000 56.00000 Total51.216674.866674.93333 3.56667 51.93333 Group Standard Deviations 15.297181.715941.888561.19722 8.09732 26.002312.357022.485511.50555 9.25263 38.614341.197221.663331.13529 7.60117 Total12.795231.978042.099811.33089 8.57395 Pooled Within-Groups Correlation Matrix INCOMETRAVELVACATIONHSIZEAGE INCOME1.00000 TRAVEL0.051201.00000 VACATION0.306810.035881.00000 HSIZE0.380500.004740.22080 1.00000 AGE-0.20939-0.34022 -0.01326-0.02512 1.00000 Cont.

28. Copyright © 2010 Pearson Education, Inc. 18-28 Results of Three-Group Discriminant Analysis Table 18.5, cont. Wilks' (U-statistic) and univariate F ratio with 2 and 27 degrees of freedom. Variable Wilks' Lambda FSignificance INCOME 0.26215 38.00 0.0000 TRAVEL 0.78790 3.634 0.0400 VACATION 0.88060 1.830 0.1797 HSIZE 0.87411 1.944 0.1626 AGE 0.88214 1.804 0.1840 CANONICAL DISCRIMINANT FUNCTIONS % of Cum Canonical After Wilks' FunctionEigenvalue Variance %Correlation Function Chi-square df Significance : 0 0.1664 44.83110 0.00 1* 3.8190 93.93 93.93 0.8902 : 1 0.8020 5.517 4 0.24 2* 0.2469 6.07 100.00 0.4450 : * marks the two canonical discriminant functions remaining in the analysis. Standardized Canonical Discriminant Function Coefficients FUNC 1 FUNC 2 INCOME 1.04740 -0.42076 TRAVEL 0.33991 0.76851 VACATION -0.14198 0.53354 HSIZE-0.16317 0.12932 AGE 0.49474 0.52447 Cont.

29. Copyright © 2010 Pearson Education, Inc. 18-29 Results of Three-Group Discriminant Analysis Table 18.5, cont. Structure Matrix: Pooled within-groups correlations between discriminating variables and canonical discriminant functions (variables ordered by size of correlation within function) FUNC 1 FUNC 2 INCOME0.85556* -0.27833 HSIZE0.19319* 0.07749 VACATION0.21935 0.58829* TRAVEL0.14899 0.45362* AGE0.16576 0.34079* Unstandardized canonical discriminant function coefficients FUNC 1 FUNC 2 INCOME0.1542658 -0.6197148E-01 TRAVEL0.1867977 0.4223430 VACATION-0.6952264E-010.2612652 HSIZE-0.1265334 0.1002796 AGE 0.5928055E-010.6284206E-01 (constant)-11.09442 -3.791600 Canonical discriminant functions evaluated at group means (group centroids) GroupFUNC 1 FUNC 2 1-2.04100 0.41847 2-0.40479 -0.65867 3 2.44578 0.24020 Cont.

30. Copyright © 2010 Pearson Education, Inc. 18-30 Results of Three-Group Discriminant Analysis Table 18.5, cont. Classification Results: PredictedGroup Membership Actual Group No. of Cases 123 Group 110910 90.0%10.0%0.0% Group210190 10.0%90.0%0.0% Group310028 0.0%20.0%80.0% Percent of grouped cases correctly classified: 86.67% Classification results for cases not selected for use in the analysis PredictedGroup Membership Actual Group No. of Cases123 Group14310 75.0%25.0%0.0% Group24031 0.0%75.0%25.0% Group34103 25.0%0.0%75.0% Percent of grouped cases correctly classified: 75.00%

31. Copyright © 2010 Pearson Education, Inc. 18-31 All-Groups Scattergram Fig. 18.3 -4.0 Across: Function 1 Down: Function 2 4.0 0.0 -6.0 4.0 0.0-2.0-4.0 2.0 6.0 1 1 1 1 1 1 1 1 1 2 1 2 2 2 2 2 3 3 3 3 3 3 3 2 3 * * * * indicates a group centroid

32. Copyright © 2010 Pearson Education, Inc. 18-32 Territorial Map Fig. 18.4 -4.0 Across: Function 1 Down: Function 2 4.0 0.0 -6.0 4.0 0.0-2.0-4.0 2.0 6.0 1 1 3 * -8.0 8.0 1 3 1 3 1 3 1 3 1 3 1 3 1 3 1 1 2 3 1 1 2 2 3 3 1 1 2 2 1 1 1 2 2 2 2 3 3 1 1 1 2 2 1 1 2 2 1 1 1 2 2 1 1 2 2 1 1 1 2 2 1 1 2 2 2 2 2 3 2 3 3 2 2 3 3 2 2 3 2 2 3 3 2 3 3 * * * Indicates a group centroid

33. Copyright © 2010 Pearson Education, Inc. 18-33 Stepwise Discriminant Analysis Stepwise discriminant analysis is analogous to stepwise multiple regression (see Chapter 17) in that the predictors are entered sequentially based on their ability to discriminate between the groups. An F ratio is calculated for each predictor by conducting a univariate analysis of variance in which the groups are treated as the categorical variable and the predictor as the criterion variable. The predictor with the highest F ratio is the first to be selected for inclusion in the discriminant function, if it meets certain significance and tolerance criteria. A second predictor is added based on the highest adjusted or partial F ratio, taking into account the predictor already selected.

34. Copyright © 2010 Pearson Education, Inc. 18-34 Stepwise Discriminant Analysis Each predictor selected is tested for retention based on its association with other predictors selected. The process of selection and retention is continued until all predictors meeting the significance criteria for inclusion and retention have been entered in the discriminant function. The selection of the stepwise procedure is based on the optimizing criterion adopted. The Mahalanobis procedure is based on maximizing a generalized measure of the distance between the two closest groups. The order in which the variables were selected also indicates their importance in discriminating between the groups.

35. Copyright © 2010 Pearson Education, Inc. 18-35 The Logit Model The dependent variable is binary and there are several independent variables that are metric The binary logit model commonly deals with the issue of how likely is an observation to belong to each group It estimates the probability of an observation belonging to a particular group

36. Copyright © 2010 Pearson Education, Inc. 18-36

37. Copyright © 2010 Pearson Education, Inc. 18-37 Binary Logit Model Formulation The probability of success may be modeled using the logit model as: Or i n i e X a P P          1 log i    i  0 X a X a X aa kk e P P        ... 1 log 22110

38. Copyright © 2010 Pearson Education, Inc. 18-38 Model Formulation )exp(1 ) 0 0 X a X a i k i i i k i i P       Where: P = Probability of success X i = Independent variable i a i = parameter to be estimated.

39. Copyright © 2010 Pearson Education, Inc. 18-39 Properties of the Logit Model Although X i may vary from to, P is constrained to lie between 0 and 1. When X i approaches, P approaches 0. When X i approaches, P approaches 1. When OLS regression is used, P is not constrained to lie between 0 and 1.

40. Copyright © 2010 Pearson Education, Inc. 18-40 Estimation and Model Fit The estimation procedure is called the maximum likelihood method. Fit: Cox & Snell R Square and Nagelkerke R Square. Both these measures are similar to R2 in multiple regression. The Cox & Snell R Square can not equal 1.0, even if the fit is perfect. This limitation is overcome by the Nagelkerke R Square. Compare predicted and actual values of Y to determine the percentage of correct predictions.

41. Copyright © 2010 Pearson Education, Inc. 18-41 Significance Testing

42. Copyright © 2010 Pearson Education, Inc. 18-42 Interpretation of Coefficients If X i is increased by one unit, the log odds will change by a i units, when the effect of other independent variables is held constant. The sign of a i will determine whether the probability increases (if the sign is positive) or decreases (if the sign is negative) by this amount.

43. Copyright © 2010 Pearson Education, Inc. 18-43 Explaining Brand Loyalty Table 18.6

44. Copyright © 2010 Pearson Education, Inc. 18-44 Results of Logistic Regression Table 18.7

45. Copyright © 2010 Pearson Education, Inc. 18-45 Results of Logistic Regression Table 18.7, cont. Variables in the Equation a Variable(s) entered on step 1: Brand, Product, Shopping. a. 1.274.4797.0751.0083.575.186.322.3351.5631.205.590.4911.4421.2301.804 -8.6423.3466.6721.010.000 Brand Product Shopping Constant Step 1 BS.E.WalddfSig.Exp(B) Classification Table a The cut value is.500 a. 12380.0 31280.0 Observed Not Loyal Loyal Loyalty to the Brand Overall Percentage Step 1 Not LoyalLoyal Loyalty to the Brand Percentage Correct Predicted

46. Copyright © 2010 Pearson Education, Inc. 18-46 SPSS Windows The DISCRIMINANT program performs both two-group and multiple discriminant analysis. To select this procedure using SPSS for Windows, click: Analyze>Classify>Discriminant … To run logit analysis or logistic regression using SPSS for Windows, click: Analyze > Regression>Binary Logistic 

47. Copyright © 2010 Pearson Education, Inc. 18-47 SPSS Windows: Two-Group Discriminant 1.Select ANALYZE from the SPSS menu bar. 2.Click CLASSIFY and then DISCRIMINANT. 3.Move “visit” into the GROUPING VARIABLE box. 4.Click DEFINE RANGE. Enter 1 for MINIMUM and 2 for MAXIMUM. Click CONTINUE. 5.Move “income,” “travel,” “vacation,” “hsize,” and “age” into the INDEPENDENTS box. 6.Select ENTER INDEPENDENTS TOGETHER (default option). 7.Click on STATISTICS. In the pop-up window, in the DESCRIPTIVES box check MEANS and UNIVARIATE ANOVAS. In the MATRICES box check WITHIN-GROUP CORRELATIONS. Click CONTINUE. 8.Click CLASSIFY... In the pop-up window in the PRIOR PROBABILITIES box, check ALL GROUPS EQUAL (default). In the DISPLAY box, check SUMMARY TABLE and LEAVE-ONE-OUT CLASSIFICATION. In the USE COVARIANCE MATRIX box, check WITHIN-GROUPS. Click CONTINUE. 9.Click OK.

48. Copyright © 2010 Pearson Education, Inc. 18-48 SPSS Windows: Logit Analysis 1.Select ANALYZE from the SPSS menu bar. 2.Click REGRESSION and then BINARY LOGISTIC. 3.Move “Loyalty to the Brand [Loyalty]” into the DEPENDENT VARIABLE box. 4.Move “Attitude toward the Brand [Brand],” “Attitude toward the Product category [Product],” and “Attitude toward Shopping [Shopping],” into the COVARIATES (S box.) 5.Select ENTER for METHOD (default option). 6.Click OK.

49. Copyright © 2010 Pearson Education, Inc. 18-49 SAS Learning Edition Both two-group and multiple discriminant analysis can be performed using the Discriminant Analysis task within SAS Learning Edition. To select this task, click: Analyze>Classify>Discriminant Analysis To run logit analysis or logistic regression using SAS Learning Edition, click: Analyze > Regression > Logistic

50. Copyright © 2010 Pearson Education, Inc. 18-50 SAS Learning Edition : Two-Group Discriminant 1.Select ANALYZE from the SAS Learning Edition menu bar. 2.Click Multivariate and then Discriminant Analysis. 3.Move VISIT to the classification variable task role. 4.Move INCOME, TRAVEL, VACATION, HSIZE, and AGE to the analysis variables task role. 5.Click Options. 6.Select Univariate test for equality of class means and Summary results of cross validation classification. (If means are desired, then use the Summary Statistics task.) 7.Click the Preview Code button. 8.Click the Insert Code… button. 9.Double click to insert code before the CROSSVALIDATE option. 10.Add the CAN and PCORR options in the popup box and then click OK. 11.Click OK and then close the Preview Code window. 12.Click Run.

51. Copyright © 2010 Pearson Education, Inc. 18-51 SAS Learning Edition: Logit Analysis 1.Select ANALYZE from the SAS Learning Edition menu bar. 2.Click Regression and then Logistic. 3.Move LOYALTY to the Dependent variable task role. 4.Move BRAND, PRODUCT, and SHOPPING to the Quantitative variables task role. 5.Select Model Effects. 6.Choose BRAND, PRODUCT, and SHOPPING as Main Effects. 7.Select Model Options. 8.Check Show classification table and enter 0.5 as the critical probability value. 9.Click Run.

52. Copyright © 2010 Pearson Education, Inc. 18-52

53. Copyright © 2010 Pearson Education, Inc. 18-53

54. Copyright © 2010 Pearson Education, Inc. 18-54 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. Copyright © 2010 Pearson Education, Inc.