To analyze the relationship between the feature "personal_status" and the task of determining whether a person receives credit or not, we can examine the distribution of different personal status values for each target class. Here is the analysis:

For the target class "yes" (person receives credit):
- Possible values of feature "personal_status" are: "male single", "female div/dep/mar", "male div/sep", "male mar/wid".

For the target class "no" (person does not receive credit):
- Possible values of feature "personal_status" are: "male single", "female div/dep/mar", "male div/sep", "male mar/wid".

Based on this analysis, we can create the following dictionary:

```json
{
	"yes": ["male single", "female div/dep/mar", "male div/sep", "male mar/wid"],
	"no": ["male single", "female div/dep/mar", "male div/sep", "male mar/wid"]
}
```

Please note that both target classes have the same possible values for the feature "personal_status", and all the values are included in the dictionary.