Based on my prior knowledge, the "residence_since" feature represents the number of years a person has been living at their current residence. To analyze the relationship between this feature and the task of whether the person receives a credit or not, we can look at the distribution of "residence_since" values for the two target classes: "yes" and "no".

Here is the analysis of the relationship between the "residence_since" feature and the task:

- For the "yes" class (person receives credit), typical residence_since values could fall within the range of 2 to 10 years. Some possible examples are: 2.5, 4.3, 6.0, 8.7, and 10.0 years.

- For the "no" class (person does not receive credit), typical residence_since values might vary between 0 and 5 years. Some possible examples are: 0.8, 1.6, 3.2, 4.4, and 5.0 years.

Based on this analysis, here is the dictionary with typical "residence_since" values for each target class:

```json
{
	"yes": [2.5, 4.3, 6.0, 8.7, 10.0],
	"no": [0.8, 1.6, 3.2, 4.4, 5.0]
}
```