Based on prior knowledge, we can assume that the number of existing credits at a bank could have some relationship with whether a person receives credit or not. To analyze this relationship, we will compare the distribution of the "existing_credits" feature for both target classes ("yes" and "no").

Here is the dictionary with the analysis:

```json
{
	"yes": [1.0, 2.0, 3.0, 4.0, 5.0],  
	"no": [0.0, 1.0, 2.0, 3.0, 4.0] 
}
```

This dictionary indicates the possible values of the "existing_credits" feature for each target class. In the target class "yes", typical values for the "existing_credits" feature are 1.0, 2.0, 3.0, 4.0, and 5.0. In the target class "no", typical values for the "existing_credits" feature are 0.0, 1.0, 2.0, 3.0, and 4.0.

Please note that these values are just hypothetical examples based on the assumption that the "existing_credits" feature might influence the likelihood of receiving credit. The actual relationship between the feature and the target variable should be verified using appropriate statistical analysis techniques.