Based on prior knowledge, let's analyze the relationship between the feature "residence_since" and the task of whether a person receives credit or not. 

Analyzing the feature, we can make the assumption that generally, individuals who have stayed at one residence for a longer period of time may have a higher likelihood of receiving credit. However, this relationship may not hold true in all cases, as it can also depend on other factors such as income stability or credit history.

To create the dictionary as requested, we will include 5 typical values of the "residence_since" feature for each target class ('yes' and 'no'):

```json
{
	"yes": [2.5, 4.0, 3.0, 5.0, 6.0],
	"no": [1.0, 1.5, 2.0, 0.5, 2.8]
}
```

Please note that these values are hypothetical and may not accurately represent the actual data. Actual analysis should be done on the dataset using appropriate statistical methods to determine the relationship between the feature and target variable.