Analyzing the data

In the variables list, only the variables in scale measurement are displayed.

The analysis uses the variables in bankloan.sav dataset such as, age of customers in years, Years with current employer, Years at current address, Household income in thousands, Debt to income ratio, Credit card debt in thousands, Other debt in thousands, Predicted default model 1, Predicted default model 2, and Predicted default model 3.

1. Go to the menu bar and click Analyze > Descriptive Statistics > Normality Analysis
2. Select the variables for which you require normality check.
3. Go to Tests and Plots tab, select the required tests and plots for checking normality.
4. Click OK.
5. From the Outliers tab, choose the desired outlier analysis. Select Show multivariate outliers to produce a table of outliers and a corresponding Q-Q plot. An ID variable must be specified for this output in order to identify the outlier points. The id values must be unique across cases. The id variable must not be included in the distribution list.
6. Choose either Quantile Mahalanobis Distance or Adjusted Mahalanobis Distance for the outlier detection method. Select the number of outliers to display. The table lists these in descending order of distance.

7. Click OK.