Based on my prior knowledge, I can analyze the relationship between the feature "existing_credits" and the task of determining whether a person receives credit or not. Here is the dictionary with the required details:

```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 analysis, I assumed that a person is more likely to receive credit when they have fewer existing credits at the bank. Thus, the "yes" class includes values such as 0.0, 1.0, 2.0, 3.0, and 4.0, indicating that individuals with few existing credits are more likely to receive credit. On the other hand, the "no" class includes values such as 1.0, 2.0, 3.0, 4.0, and 5.0, representing individuals with a higher number of existing credits who are less likely to receive credit.