Based on prior knowledge, the feature "residence_since" represents the duration of the person's current residence. To analyze its relationship with the target variable "Does this person receive a credit?", we need to consider typical residence_since values for both classes ('yes' and 'no').

Assuming shorter residence durations might indicate higher risks or instability, and longer residence durations might imply stability and responsibility, we can make some assumptions about the relationship between the feature and the target:

1. For the 'yes' class (person receives a credit), typical residence_since values could include:
   - 1.5 years
   - 2.5 years
   - 3.5 years
   - 4.5 years
   - 5.5 years

2. For the 'no' class (person does not receive a credit), typical residence_since values could include:
   - 0.5 years
   - 1 year
   - 2 years
   - 3 years
   - 4 years

Based on these assumptions, we can create the following dictionary:

```json
{
	"yes": [1.5, 2.5, 3.5, 4.5, 5.5],
	"no": [0.5, 1, 2, 3, 4]
}
```