Based on prior knowledge, the feature "residence_since" represents the number of years a person has been living at their current residence. To analyze its relationship with the task of whether the person receives credit or not, we can consider the following:

1. Individuals who have been living at their current residence for a longer period might be more favorable candidates for receiving credit, as it indicates stability and reliability. Therefore, higher values of "residence_since" might be associated with a higher likelihood of receiving credit.

2. On the other hand, individuals who have recently moved to a new residence might be deemed less reliable and potentially have a higher risk of defaulting on credit payments. Hence, lower values of "residence_since" could be correlated with a lower likelihood of receiving credit.

Based on this analysis, let's create a dictionary with the requested format, including 5 typical residence_since values for each target class:

```json
{
	"yes": [4.0, 8.0, 10.0, 12.0, 15.0],
	"no": [1.0, 2.0, 3.0, 5.0, 6.0]
}
```

Please note that these examples are hypothetical and should be validated using the actual dataset.