Based on prior knowledge, we can hypothesize that the job feature may have an impact on whether a person receives a credit or not. Different job categories may indicate different levels of income, stability, and financial capacity, which could affect the creditworthiness of individuals.

To analyze the relationship between the job feature and the target task (whether a person receives credit or not), we can examine the distribution of job categories for each target class.

Here is the analysis and resulting dictionary:

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

Explanation:
- For the target class "yes" (person receives credit), the job categories "skilled" and "high qualif/self emp/mgmt" are listed as values in the dictionary. These job categories may indicate individuals with higher income or stable employment, making them more likely to receive credit.
- For the target class "no" (person does not receive credit), the job categories "unskilled resident" and "unemp/unskilled non res" are listed as values in the dictionary. These job categories may indicate individuals with lower income or unstable employment, making them less likely to receive credit.

Note:
- I did not include the category "unemployed" as it is a bit ambiguous and can be included within the "unemp/unskilled non res" category.
- If there are other job categories not mentioned here, they are either not significantly related to the target task or their inclusion would not provide valuable insights into the relationship between the feature and target.