To analyze the relationship between the "credit_amount" feature and the task of determining whether a person receives credit or not, we can use prior knowledge to identify typical ranges of credit amounts for each target class.

Based on common knowledge, I will assume that smaller credit amounts are more likely to be approved while larger credit amounts may have a higher chance of being rejected.

For the "yes" class (receives credit), I will consider the following typical credit_amount values:
- 1000.0
- 1500.0
- 2000.0
- 2500.0
- 3000.0

For the "no" class (does not receive credit), I will consider the following typical credit_amount values:
- 5000.0
- 6000.0
- 7000.0
- 8000.0
- 9000.0

Based on this analysis, the dictionary representation will be:

```json
{
	"yes": [1000.0, 1500.0, 2000.0, 2500.0, 3000.0],
	"no": [5000.0, 6000.0, 7000.0, 8000.0, 9000.0]
}
```