Based on prior knowledge, we can analyze the relationship between the feature "existing_credits" and the task of whether a person receives credit or not.

We can assume that the number of existing credits at a bank may have an impact on the credit decision. Higher number of existing credits might indicate a higher risk for the bank, and hence might be associated with a higher chance of being denied credit.

To analyze this further, we can look at typical values of "existing_credits" for each target class ('yes' or 'no'). Based on the task, the target variable would be whether the person receives credit or not.

Let's 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": [1.0, 2.0, 3.0, 4.0, 5.0]
}
```

These lists represent typical values of "existing_credits" for individuals who did receive credit ('yes') and those who did not receive credit ('no') respectively. The values are presented as floats in the lists.