Based on my prior knowledge and experience, I would expect that the feature "residence_since" could have some relationship with the target variable "Does this person receive a credit?". 

To analyze this relationship, I will consider the following:

1. I will examine the distribution of residence_since values for each target class to identify any notable patterns or differences.
2. I will compare the average residence_since values for the two target classes to see if there is a significant difference.
3. I will also look for any specific ranges of residence_since values that are more prevalent or indicative of a particular target class.

Here is the dictionary with the analysis details for the given feature and task:

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

Based on my analysis, I have identified five typical residence_since values for each target class ('yes' and 'no'). These values are presented as floats in the lists. Please note that these specific values are just hypothetical and may vary depending on the actual data.