Based on prior knowledge and common sense, it is possible to analyze the relationship between the "job" feature and the target variable "Does this person receive a credit?". Here are some observations:

1. Skilled Job: It is likely that individuals with skilled jobs have a higher probability of receiving credit. Skilled jobs typically imply a higher level of income stability and financial security, which makes them more reliable candidates for credit approval.

2. Unskilled Resident Job: Individuals with unskilled resident jobs may have a lower chance of receiving credit. This category typically includes low-paying and unstable jobs, which can negatively impact creditworthiness.

3. High Qualif/Self Emp/Mgmt Job: People in this job category are usually professionals, self-employed, or in higher management positions. It is probable that individuals with these jobs have a higher credit approval rate compared to those in other categories.

4. Unemp/Unskilled Non-Res Job: Unemployed individuals or those with unskilled non-resident jobs may have a lower likelihood of receiving credit. Lack of employment or being in an unskilled non-resident position can raise concerns about repayment capacity.

Based on these insights, let's create the desired dictionary:

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

Note that the values "skilled" and "high qualif/self emp/mgmt" are included in the "yes" list since individuals with these job types are more likely to receive credit. The "no" list includes "unskilled resident" and "unemp/unskilled non res" as they are more likely to be associated with a lower probability of receiving credit.