Based on prior knowledge, we can analyze the relationship between the feature residence_since and the task of whether the person receives a credit or not. It is reasonable to assume that individuals who have been residing at their current residence for a longer duration are more likely to receive a credit than those who have just moved in. 

Here is a dictionary that represents the relationship between the residence_since feature and the target task:

```json
{
	"yes": [2.0, 3.0, 4.0, 5.0, 6.0], 
	"no": [0.0, 1.0, 7.0, 8.0, 9.0]  
}
```

This dictionary shows typical residence_since values for the target classes "yes" and "no". The "yes" class includes values such as 2.0, 3.0, 4.0, 5.0, and 6.0, indicating that individuals who have been residing at their current residence for these durations are more likely to receive a credit. On the other hand, the "no" class includes values such as 0.0, 1.0, 7.0, 8.0, and 9.0, suggesting that individuals who have recently moved in or have been residing for a longer duration are less likely to receive a credit.