Based on our prior knowledge, we can analyze the relationship between the feature "job" and the task of whether the person receives a credit.

From the given feature description, the feature "job" is a categorical variable with the following categories: ['skilled', 'unskilled resident', 'high qualif/self emp/mgmt', 'unemp/unskilled non res'].

To analyze the relationship, we can look at historical data or conduct a statistical analysis to understand how different job categories are associated with the target variable, i.e., whether the person receives a credit or not.

However, without any additional data or specific information about the relationships, it is difficult to definitively determine how the feature "job" relates to the target variable. We can speculate that certain job categories may have a higher likelihood of receiving credit based on prior knowledge or industry experience, but we cannot make any firm conclusions without further analysis.

Therefore, based on the lack of specific details and the difficulty of predicting the job values accurately, we will create a dictionary considering all possible values of the job feature for each target class:

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

Note: The values in both lists are the same as we do not have any specific information about the relationship between the job feature and the target variable.