Based on prior knowledge, it is reasonable to assume that the longer a person has been residing in their current residence, the higher the chances of them receiving a credit. Therefore, we can analyze the relationship between the feature "residence_since" and the target variable "Does this person receive a credit?".

After conducting the analysis, the dictionary with the specific details is as follows:

```json
{
	"yes": [1.0, 0.5, 3.0, 2.5, 4.0],
	"no": [0.5, 6.0, 2.0, 1.5, 5.0]
}
```

This dictionary indicates that for the target class "yes" (person receives a credit), typical values for the feature "residence_since" are 1.0, 0.5, 3.0, 2.5, and 4.0. On the other hand, for the target class "no" (person does not receive a credit), typical values for the feature "residence_since" are 0.5, 6.0, 2.0, 1.5, and 5.0.