Based on my prior knowledge, the relationship between the feature "credit_amount" and the task of whether a person receives credit can be analyzed as follows:

1. Generally, individuals who receive credit may have a wider range of credit amounts compared to those who do not receive credit. Therefore, it is likely that there will be a greater variation in the credit_amount for the "yes" target class.

2. However, it is also possible that there might be specific ranges of credit amounts that are more likely to result in receiving credit. For example, individuals with very low credit_amounts may be more likely to be denied credit due to perceived risk, while individuals with very high credit amounts may be more likely to be approved.

Based on this analysis, the dictionary with the relevant information can be generated as follows:

```json
{
	"yes": [5000.0, 10000.0, 15000.0, 20000.0, 25000.0],
	"no": [1000.0, 2000.0, 3000.0, 4000.0, 5000.0]
}
```

Please note that the provided values are for illustrative purposes only and may not represent the actual data distribution.