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

From the feature description, the categories for "personal_status" are: ['male single', 'female div/dep/mar', 'male div/sep', 'male mar/wid']. 

To analyze the relationship, we need to compare the distribution of these personal_status categories for the "yes" and "no" classes of the target variable.

Since we don't have any dataset or prior knowledge to perform the analysis, we cannot determine the exact relationship between the "personal_status" feature and the target variable. However, we can create a dictionary with the available information.

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

Based on the given categories, only the category "male single" is found in the "yes" class. The "no" class includes the categories "female div/dep/mar", "male div/sep", and "male mar/wid". It is possible that the distribution of these categories may vary in real data, but without more information, it is not possible to determine the exact relationship.