Based on my prior knowledge, the feature "existing_credits" represents the number of existing credits at the bank for a person. To analyze the relationship between this feature and the task of whether the person receives a credit or not, we can consider the following:

1. It is possible that individuals with a higher number of existing credits at the bank may have a higher probability of receiving a credit. This could be because having multiple existing credits shows a positive credit history and reliability.

2. On the other hand, individuals with a very high number of existing credits might have a lower probability of receiving a credit. This could be due to the bank's risk assessment, as too many credits could indicate a higher likelihood of defaulting on payments.

Based on this analysis, we can create a dictionary with the typical values of "existing_credits" for each target class:

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

Note: The specific values for "existing_credits" in this dictionary are arbitrary and should be replaced with actual values from the dataset during implementation.