Based on prior knowledge, the number of existing credits at a bank may have a relationship with whether or not a person receives credit. However, without specific information about the threshold or criteria used by the bank to grant or deny credit, it is difficult to determine the appropriate ranges of values for the feature "existing_credits". 

To conduct the analysis, we can examine the values of the feature "existing_credits" for both the "yes" and "no" classes of the target variable. While we do not have access to the actual dataset, we can assume five typical values for each class. 

Analyzing the relationship between "existing_credits" and the target variable, we might generate a dictionary as follows:

```json
{
  "yes": [1.0, 2.0, 3.0, 4.0, 5.0],
  "no": [0.0, 1.0, 2.0, 3.0, 4.0]
}
```
In this example, we assume that for the "yes" class (i.e., the person receives credit), typical values for "existing_credits" might be between 1 and 5, while for the "no" class (i.e., the person does not receive credit), typical values might range from 0 to 4.

Please note that these values are only hypothetical and a real analysis should be conducted using the actual dataset.