Based on prior knowledge, we can assume that the feature "residence_since" refers to the number of years a person has been living at their current residence.

To analyze the relationship between the feature "residence_since" and the task of determining whether a person receives credit or not, we can consider the following:

1. Stability: People who have been living at the same residence for a longer period of time may be considered more stable and responsible, which could increase their chances of receiving credit.

2. Commitment: Individuals who have resided at the same place for a significant number of years may indicate a higher level of commitment and consistency, which can be positively correlated with creditworthiness.

Based on this analysis, we can create a dictionary with the typical residence_since values for each target class ('yes' and 'no'):

```json
{
	"yes": [2.0, 3.0, 4.0, 5.0, 6.0],
	"no": [1.0, 2.5, 6.5, 7.0, 8.0]
}
```

Please note that these values are just examples and should be adjusted based on the specific dataset and domain knowledge.