Based on the given feature "personal_status" and the task of determining whether a person receives credit or not, we can analyze the relationship between the feature and the target variable. 

Looking at the feature, we can observe that it consists of different categories related to personal status and sex. 

To analyze the relationship, we would need to examine the distribution of each category within the target classes (yes and no). We can then determine which categories are more prevalent for each target class.

Since we do not have the data to perform the analysis, we can provide a general approach and example output for the dictionary:

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

Please keep in mind that the actual values might differ based on the specific dataset and the distribution of the personal_status feature within the target classes.