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

By exploring the feature "job" and its categories, we can make some assumptions about the relationship:

- "skilled": It is likely that individuals with skilled jobs have a higher chance of receiving credit.
- "unskilled resident": Individuals with unskilled resident jobs may have a lower chance of receiving credit compared to those with skilled jobs.
- "high qualif/self emp/mgmt": Those in high qualification, self-employed or management jobs may have a higher chance of receiving credit.
- "unemp/unskilled non res": Individuals who are unemployed or have unskilled non-resident jobs may have a lower chance of receiving credit.

Based on these assumptions, we can create a dictionary with the possible values of the feature "job" for each target class "yes" and "no".

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

This dictionary includes the values of "job" that are likely to have a significant impact on whether a person receives credit or not.