Based on prior knowledge, we can hypothesize that the credit amount would have a significant impact on whether a person receives credit or not. Generally, higher credit amounts might indicate a higher risk for lenders, leading to a lower chance of receiving credit. 

To analyze the relationship, we can examine the distribution of credit_amount values for both the "yes" and "no" classes. Let's assume the credit_amount ranges are as follows:
- Low: 0-500
- Medium: 501-1000
- High: 1001-5000
- Very High: 5001-10000
- Extremely High: 10001 and above

Now, let's create a dictionary as requested:

```json
{
	"yes": [1000.0, 1500.0, 2000.0, 2500.0, 3000.0],
	"no": [6000.0, 8000.0, 9000.0, 12000.0, 15000.0]
}
```

In this example, we included 5 typical credit_amount values for each target class. Please note that these values are arbitrary and should be determined based on the actual data and analysis.