Based on the task of determining whether a person receives a credit or not and the feature of credit_amount, we can analyze the relationship between the two. 

Typically, higher credit amounts might indicate a higher likelihood of receiving credit as the person has a higher capacity to repay. However, there may be a threshold beyond which higher credit amounts could lead to a lower probability of receiving credit. 

To create the dictionary, we should consider both the lower and upper ranges of credit_amount for both target classes.

Here is the dictionary with typical credit_amount values for each target class:

```json
{
	"yes": [1000.0, 2000.0, 3000.0, 4000.0, 5000.0],
	"no": [500.0, 1000.0, 1500.0, 2000.0, 2500.0]
}
```

Please note that these values are just examples and the actual ranges and values may vary depending on the specific dataset and analysis.