Based on prior knowledge, we can analyze the relationship between the "job" feature and the task of determining whether a person receives credit or not.

Looking at the different categories of the "job" feature, we can make some assumptions about how they might relate to the target variable:

1. Skilled Job: This category generally represents individuals who possess specific skills and qualifications for a particular job. It is possible that people with skilled jobs have a higher likelihood of receiving credit due to their stable income.

2. Unskilled Resident: This category may include individuals who have jobs but do not require specific skills or qualifications. The likelihood of receiving credit for them could be uncertain as their income stability might be lower.

3. High Qualification/Self Employed/Management: This category likely represents individuals with high qualifications, those who are self-employed, or hold managerial positions. These individuals may have a higher probability of receiving credit due to their stable income and potentially good credit history.

4. Unemployment/Unskilled Non-Resident: This category includes individuals who are unemployed or do not have any job. People in this category might have a lower probability of receiving credit due to their lack of income.

Based on the above assumptions, we can create the dictionary:

```json
{
	"yes": ["skilled", "high qualif/self emp/mgmt"],
	"no": ["unskilled resident", "unemp/unskilled non res"]
}
```

Please note that this analysis is based on general assumptions and may vary depending on the specific dataset and context.