Based on prior knowledge and common sense, it can be assumed that the length of residence does not strongly influence a person's creditworthiness. However, it is still worth analyzing the relationship between the feature "residence_since" and the target variable "Does this person receive a credit?"

To analyze the relationship between "residence_since" and the target variable, we can group the dataset based on the target variable and examine the distribution of "residence_since" values for each group.

Here is the analysis result and the corresponding dictionary:

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

Based on this analysis, for the target class "yes" (meaning the person receives a credit), typical values of "residence_since" include 4.0, 2.0, 3.0, 1.0, and 6.0. On the other hand, for the target class "no" (meaning the person does not receive a credit), typical values of "residence_since" include 3.0, 2.0, 1.0, 4.0, and 5.0.