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

To do this, we can assume that the length of time a person has resided at their current residence might have an influence on whether they receive credit or not. Typically, a longer duration of residence might indicate stability, financial security, and a lower risk of defaulting on credit.

Based on this assumption, we can create the following dictionary:

```json
{
	"yes": [1.0, 3.0, 5.0, 7.0, 10.0],
	"no": [2.0, 4.0, 6.0, 8.0, 9.0]
}
```

In this dictionary, we include 5 typical values for each target class ('yes' and 'no') for the feature "residence_since". The values for the "yes" class are [1.0, 3.0, 5.0, 7.0, 10.0], indicating that individuals who receive credit have resided at their current residence for these durations. The values for the "no" class are [2.0, 4.0, 6.0, 8.0, 9.0], indicating that individuals who do not receive credit have resided for these durations.

Please note that these values are just fictional examples and should not be considered as definitive. The actual analysis and determination of appropriate ranges should be performed using a dataset or through domain-specific knowledge.