Based on prior knowledge, we can analyze the relationship between the 'credit_amount' feature and the task of whether a person receives credit or not. Generally, the credit amount is an important factor in determining whether a person is approved for credit or not. Higher credit amounts might indicate a higher risk for lenders, while lower credit amounts might be considered less risky.

It is important to note that the specific credit amount ranges that determine approval or denial of credit might vary depending on the specific context and dataset being analyzed. However, we can still provide a general analysis and include 5 typical credit_amount values for each target class.

Here is the generated dictionary:

```json
{
	"yes": [1000.0, 1500.0, 2000.0, 2500.0, 3000.0],
	"no": [500.0, 750.0, 1200.0, 1800.0, 2200.0]
}
```

In this dictionary, the "yes" key represents the target class where the person receives credit, and the "no" key represents the target class where the person does not receive credit. The values associated with each key are the credit amount values that are typical for each target class.