Based on the feature description, it seems that the personal_status and sex variables are categorical variables describing the personal status of individuals. The task is to determine whether a person receives credit or not.

To analyze the relationship between the feature and the task, we can start by examining the possible values of the personal_status variable for each target class.

1. Target class: Yes
   - From the given feature description, the possible values of personal_status for the target class "Yes" are:
     - ['male single']

2. Target class: No
   - From the given feature description, the possible values of personal_status for the target class "No" are:
     - ['female div/dep/mar', 'male div/sep', 'male mar/wid']

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

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

Note that this analysis assumes that the given feature description is complete and representative of the entire dataset. It is possible that there may be additional values for personal_status in the dataset that are not mentioned in the description.