Based on my prior knowledge, the feature "job" can be a significant predictor of whether a person receives credit or not. Different types of jobs can indicate different levels of income, stability, and financial security, which in turn can affect the likelihood of being approved for credit.

To conduct the analysis and create the dictionary, I would first examine the distribution of the "job" feature for each target class (yes and no).

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

In the target class "yes" (person receives credit), the possible values of the "job" feature are "skilled" and "high qualif/self emp/mgmt". These categories likely represent individuals with better job prospects, higher incomes, or stable self-employment, which may increase their chances of obtaining credit.

In the target class "no" (person does not receive credit), the possible values of the "job" feature are "unskilled resident" and "unemp/unskilled non res". These categories might indicate lower income levels, higher unemployment rates, or less desirable job positions, which could potentially lead to credit denials.

Please note that the mentioned values are just examples based on the provided categories. There could be other specific values within each category that need to be taken into consideration during the analysis.