Based on prior knowledge and assuming that the credit_amount is usually a positive value, we can analyze the relationship between the credit_amount feature and the task of determining whether a person receives a credit or not.

Analysis:

1. If the credit_amount is low, it is more likely that the person will receive a credit because smaller amounts are easier to approve.
2. If the credit_amount is extremely high, it may indicate that the person is requesting an unusually large credit, which might decrease the chances of approval.
3. There may be a threshold value, above which the chances of approval decrease significantly. This threshold could vary depending on the specific lending institution's policies.
4. Generally, there could be a range in which credit_amount values are more evenly distributed between the "yes" and "no" classes. This range could represent values where the decision is based on additional factors, rather than solely on the credit_amount.

Based on this analysis, we can now create a dictionary with specific details:

```json
{
	"yes": [1000.0, 2000.0, 3000.0, 5000.0, 8000.0],
	"no": [15000.0, 20000.0, 25000.0, 30000.0, 50000.0]
}
```

Please note that these values are just examples and may vary depending on the actual dataset and the lending institution's policies.