Based on the given feature "personal_status", we can observe that it provides information about the personal status of the person. The possible values of this feature are 'male single', 'female div/dep/mar', 'male div/sep', and 'male mar/wid'.

To analyze the relationship between this feature and the task of whether the person receives a credit or not, we need to examine the distribution of the feature values for each target class (yes and no).

Let's assume we have the following data:

| personal_status | Credit |
|-----------------|--------|
| male single     | yes    |
| female div/dep/mar   | no     |
| male div/sep   | yes     |
| male mar/wid    | no     |
| female div/dep/mar   | yes     |
| male single    | yes     |
| male mar/wid   | no     |
| female div/dep/mar   | no     |
| male mar/wid   | yes     |
| male single    | yes     |

From this data, we can observe the following relationship between the feature and the target:

For the "yes" class:
- personal_status values: ['male single', 'male div/sep', 'female div/dep/mar']

For the "no" class:
- personal_status values: ['female div/dep/mar', 'male mar/wid']

Therefore, the dictionary representing the relationship between the feature and the target would be:

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

Note that we only include the personal_status values that appear in the data for each target class, and exclude those that do not appear.