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

To analyze this relationship, we can look at typical credit amounts for individuals who receive credit versus those who do not. Let's assume that higher credit amounts may be more likely to result in receiving credit.

Based on this assumption, we can create a dictionary with typical credit_amount values for each target class:

```json
{
    "yes": [7500.0, 10000.0, 15000.0, 20000.0, 25000.0],
    "no": [500.0, 1000.0, 2000.0, 3000.0, 4000.0]
}
```

In the "yes" class, typical credit_amount values could be 7500.0, 10000.0, 15000.0, 20000.0, and 25000.0. These values represent moderate to high credit amounts that are more likely to result in receiving credit.

In the "no" class, typical credit_amount values could be 500.0, 1000.0, 2000.0, 3000.0, and 4000.0. These values represent low credit amounts that are less likely to result in receiving credit.

Please note that the specific ranges and values chosen here are based on assumptions and prior knowledge. The actual analysis may require more detailed data exploration and statistical techniques.