Based on my prior knowledge, I can analyze the relationship between the feature "existing_credits" and the task of determining whether a person receives a credit.

Analysis:
- Generally, having a low number of existing credits at the bank may indicate a higher likelihood of receiving a credit, as it suggests a lower level of existing debt.
- On the other hand, having a high number of existing credits at the bank may indicate a higher likelihood of not receiving a credit, as it suggests a higher level of existing debt and potentially higher risk.
- It is important to note that other factors, such as income, employment status, and credit history, may also play a significant role in determining whether a person receives a credit.

Based on this analysis, I will create a dictionary with possible values of the "existing_credits" feature for each target class:

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

In this example, for the target class "yes", I have included 5 typical values of "existing_credits" (0.0, 1.0, 2.0, 3.0, 4.0) that may indicate a higher likelihood of receiving a credit. For the target class "no", I have included 5 typical values of "existing_credits" (1.0, 2.0, 3.0, 4.0, 5.0) that may indicate a higher likelihood of not receiving a credit.