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 categories of "personal_status" and their descriptions:
- male single: unmarried males
- female div/dep/mar: females who are divorced, separated, or married
- male div/sep: males who are divorced or separated
- male mar/wid: males who are married or widowed

It seems that the feature "personal_status" represents the marital status of the person.

To analyze the relationship between "personal_status" and the target variable, we would need access to a dataset that includes these variables along with the target variable. By examining the distribution of "personal_status" values for those who receive credit (target class: yes) and those who do not (target class: no), we can determine if there are any patterns or trends.

Without access to a dataset, we can make some assumptions based on common knowledge. For example, it is possible that individuals who are married or widowed (male mar/wid) may have a higher chance of receiving credit due to potential stability and financial support. On the other hand, unmarried individuals (male single) or those who are divorced or separated (female div/dep/mar, male div/sep) may have a lower chance of receiving credit.

Now, let's create the dictionary assuming these assumptions:

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

Please note that these assumptions are based on general knowledge and may not accurately reflect the relationship between "personal_status" and the target variable without analyzing an actual dataset.