Based on my prior knowledge, the feature "job" is likely to have some relationship with the target variable "Does this person receive a credit?". Let's analyze this relationship in more detail:

By looking at the feature values:

- "skilled": Individuals with skilled jobs are more likely to have a stable income, which might positively influence their creditworthiness. So it's possible that if someone has a skilled job, they are more likely to receive a credit.

- "unskilled resident": These individuals might have lower income and less stable employment, which could negatively impact their creditworthiness. It is possible that if someone has an unskilled resident job, they are less likely to receive a credit.

- "high qualif/self emp/mgmt": Individuals with high qualifications or in self-employment or management positions might have higher income and more stable employment, indicating a higher creditworthiness. So it's possible that if someone has a high qualification, self-employment, or management job, they are more likely to receive a credit.

- "unemp/unskilled non res": Individuals who are unemployed or have unskilled non-resident jobs might have lower income and unstable employment, which could negatively affect their creditworthiness. It is possible that if someone is unemployed or has an unskilled non-resident job, they are less likely to receive a credit.


Based on this analysis, let's create the required dictionary:

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

Please note that this is a general analysis based on prior knowledge and assumptions. The actual relationship between the "job" feature and the target variable can be confirmed by analyzing the dataset.