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UT SW 388R7 - Multinomial Logistic Regression

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Multinomial Logistic Regression: Detecting Outliers and Validating AnalysisOutliersExampleRequest multinomial logistic regression for baseline modelSelecting the dependent variableSelecting metric independent variablesSpecifying statistics to include in the outputRequesting the classification tableCompleting the multinomial logistic regression requestClassification accuracy for all casesOutliers for the comparison of groups 1 and 3Selecting groups 1 and 3Formula for selecting groups 1 and 3Completing the selection of groups 1 and 3Binary logistic regression comparing groups 1 and 3Dependent and independent variables for the comparison of groups 1 and 3Including studentized residuals in the comparison of groups 1 and 3Slide 18Locating the case ids for outliers for groups 1 and 3Replace the selection criteriaFormula for identifying outliersCompleting the selection of outliersLocating the outliers in the data editorThe outliers in the data editorOutliers for the comparison of groups 2 and 3Selecting groups 2 and 3Formula for selecting groups 2 and 3Completing the selection of groups 2 and 3Binary logistic regression comparing groups 2 and 3Slide 30Locating the case ids for outliers for groups 2 and 3Slide 32Slide 33Slide 34Slide 35Slide 36The caseid of the outliersExcluding the outliers from the multinomial logistic regressionChanging the condition for the selectionExcluding cases identified as outliersCompleting the exclusion of the outlierMultinomial logistic regression excluding the outlierRunning the multinomial logistic regression without the outlierClassification accuracy after omitting outliers75/25% Cross-validation StrategySlide 46Restoring the outlier to the data setRestoring the outliers to the data setRe-running the multinomial logistic regression with all casesRequesting the multinomial logistic regression againOverall RelationshipIndividual relationships - 1Individual relationships - 2Individual relationships - 3Individual relationships - 4Individual relationships - 5Classification Accuracy - 1Classification Accuracy - 2Validation analysis: set the random number seedSet the random number seedValidation analysis: compute the split variableThe formula for the split variableSelecting the teaching sample - 1Selecting the teaching sample - 2Selecting the teaching sample - 3Selecting the teaching sample - 4Re-running the multinomial logistic regression with the teaching sampleSlide 68Comparing the teaching model to full model - 1Comparing the teaching model to full model - 2Comparing the teaching model to full model - 3Classification accuracy of the training sampleClassification accuracy of the holdout sampleThe log of the odds for the first groupThe log of the odds for the second groupThe log of the odds for the third groupThe probabilities for each groupGroup classificationSelecting the holdout sample - 1Selecting the holdout sample - 2Selecting the holdout sample - 3Selecting the holdout sample - 4The crosstabs classification accuracy table - 1The crosstabs classification accuracy table - 2The crosstabs classification accuracy table - 3The crosstabs classification accuracy table - 4The crosstabs classification accuracy table - 5SW388R7Data Analysis & Computers IISlide 1Multinomial Logistic Regression:Detecting Outliers and Validating AnalysisOutliersSplit-sample ValidationSW388R7Data Analysis & Computers IISlide 2OutliersMultinomial logistic regression in SPSS does not compute any diagnostic statistics. In the absence of diagnostic statistics, SPSS recommends using the Logistic Regression procedure to calculate and examine diagnostic measures.A multinomial logistic regression for three groups compares group 1 to group 3 and group 2 to group 3. To test for outliers, we will run two binary logistic regressions, using case selection to compare group 1 to group 3 and group 2 to group 3.From both of these analyses we will identify a list of cases with studentized residuals greater than ± 2.0, and test the multinomial solution without these cases. If the accuracy rate of this model is less than 2% more accurate, we will interpret the model that includes all cases.SW388R7Data Analysis & Computers IISlide 3ExampleTo demonstrate the process for detecting outliers, we will examine the relationship between the independent variables "age" [age],"highest year of school completed" [educ] and "confidence in banks and financial institutions" [confinan] and the dependent variable "opinion about spending on social security" [natsoc]. Opinion about spending on social security contains three categories: 1 too little 2 about right 3 too muchWith all cases, including those that might be identified as outliers, the accuracy rate was 63.7%. We note this to compare with the classification accuracy after removing outliers to determine which model we will interpret.SW388R7Data Analysis & Computers IISlide 4Request multinomial logistic regression for baseline modelSelect the Regression | Multinomial Logistic… command from the Analyze menu.SW388R7Data Analysis & Computers IISlide 5Selecting the dependent variableSecond, click on the right arrow button to move the dependent variable to the Dependent text box.First, highlight the dependent variable natsoc in the list of variables.SW388R7Data Analysis & Computers IISlide 6Selecting metric independent variablesMove the metric independent variables, age, educ and confinan to the Covariate(s) list box.Metric independent variables are specified as covariates in multinomial logistic regression. Metric variables can be either interval or, by convention, ordinal.SW388R7Data Analysis & Computers IISlide 7Specifying statistics to include in the outputWhile we will accept most of the SPSS defaults for the analysis, we need to specifically request the classification table.Click on the Statistics… button to make a request.SW388R7Data Analysis & Computers IISlide 8Requesting the classification tableFirst, keep the SPSS defaults for Model and Parameters.Second, mark the checkbox for the Classification table.Third, click on the Continue button to complete the request.SW388R7Data Analysis & Computers IISlide 9Completing the multinomial logistic regression requestClick on the OK button to request the output for the multinomial logistic regression.The multinomial logistic procedure supports additional commands to specify the model computed for the relationships (we will use the default main effects model), additional specifications


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UT SW 388R7 - Multinomial Logistic Regression

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