To analyze the relationship between the feature "residence_since" and the task of determining whether a person receives a credit or not, we can make some assumptions based on prior knowledge.

Typically, individuals with a longer duration of residence at their current address may be considered more stable and reliable, which could positively influence their chances of receiving credit. On the other hand, individuals with a shorter residence duration may be perceived as more transient or less settled, which could potentially decrease their chances of being granted credit.

Based on these assumptions, we can create a dictionary that includes typical values of "residence_since" for both the "yes" and "no" target classes:

```json
{
	"yes": [3.0, 5.0, 8.0, 10.0, 12.0],
	"no": [1.0, 2.0, 4.0, 6.0, 9.0]
}
```

Please note that these values are arbitrary and depend on the specific dataset and context. It is important to perform a thorough analysis using the actual data to determine the most appropriate ranges and values for the "residence_since" feature.