Based on the provided feature description and task, it is necessary to analyze the relationship between the "personal_status" feature and the target variable of whether the person receives a credit or not.

Since the target variable is binary (yes or no), we need to analyze how different values of the "personal_status" feature affect the probability of receiving a credit or not.

Based on the provided categories for the "personal_status" feature: ['male single', 'female div/dep/mar', 'male div/sep', 'male mar/wid'], we can identify the possible values for each target class:

For target class "yes" (person receives a credit):
- Possible values for "personal_status": ['male single']

For target class "no" (person does not receive a credit):
- Possible values for "personal_status": ['female div/dep/mar', 'male div/sep', 'male mar/wid']

Based on this analysis, the dictionary representing the relationship between the "personal_status" feature and the target variable can be generated as follows:

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