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

Thorough analysis:
A person's credit amount is likely to be a significant factor in determining whether they receive a credit or not. Typically, higher credit amounts may indicate a higher risk for lenders, leading to a higher likelihood of denial.

Based on this analysis, we can create the following dictionary:

```json
{
	"yes": [1000.0, 1500.0, 2000.0, 2500.0, 3000.0],
	"no": [500.0, 750.0, 800.0, 900.0, 1200.0]
}
```

This dictionary suggests that for the target class "yes" (person receives credit), typical credit_amount values could be around 1000.0, 1500.0, 2000.0, 2500.0, and 3000.0. For the target class "no" (person does not receive credit), typical credit_amount values could be around 500.0, 750.0, 800.0, 900.0, and 1200.0. These values are just examples and can vary depending on the specific dataset being analyzed.