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

Analysis:
In general, age can be a significant factor in determining creditworthiness. Younger individuals may have less established credit histories and lower incomes, making them a higher risk for lenders. Older individuals may be more financially stable and have longer credit histories, making them a lower risk. 

As requested, I will provide 5 typical age values for each target class ('yes' and 'no').

Dictionary:
```json
{
	"yes": [25.0, 35.0, 28.0, 30.0, 32.0],
	"no": [40.0, 45.0, 50.0, 55.0, 60.0]
}
```
Please note that these age values are just examples and may vary depending on the specific dataset or context.