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

The feature "residence_since" represents the number of years a person has been residing at their present residence. The assumption is that individuals who have been living at the same residence for a longer period may have more stability and higher chances of receiving credit. 

To analyze the relationship, we can examine the distribution of "residence_since" values for both the "yes" and "no" classes. Then, we can identify typical values for each class.

Here is the dictionary with the typical residence_since values for each target class:

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

Please note that the specific ranges of values for "residence_since" will depend on the dataset and the context in which it is applied. This analysis provides a general understanding of the relationship between the feature and the task.