Based on prior knowledge and understanding of the relationship between the feature "residence_since" and the task of determining if a person receives credit, we can assume that the length of time a person has been residing at their present residence may have an influence on their creditworthiness.

Here is a dictionary analyzing the relationship between the feature "residence_since" and the target variable:

```json
{
	"yes": [0.5, 2.0, 3.0, 4.0, 5.0],
	"no": [1.0, 2.5, 4.5, 6.0, 7.0]
}
```

In this case, the feature values for the target class 'yes' include 0.5, 2.0, 3.0, 4.0, and 5.0 as typical residence_since values. For the target class 'no', the typical residence_since values are 1.0, 2.5, 4.5, 6.0, and 7.0.