Based on prior knowledge, we can assume that the relationship between age and whether a person receives credit can be influenced by various factors such as income, credit history, and employment status. However, without specific data or context, it is difficult to accurately determine the appropriate ranges of age values that correspond to receiving or not receiving credit.

To provide you with an analysis, I will assume that younger individuals (e.g., under 25 years) may have a lower likelihood of receiving credit due to their limited credit history and income. On the other hand, older individuals (e.g., over 60 years) might also have a lower chance of receiving credit due to potential retirement or fixed income constraints.

Considering this assumption, here is a dictionary with typical age values for each target class:

```json
{
	"yes": [35.0, 40.0, 45.0, 50.0, 55.0],
	"no": [20.0, 25.0, 30.0, 65.0, 70.0]
}
```

Please note that these values are just examples based on assumptions, and the real relationship between age and receiving credit can only be determined with access to specific data.