Based on the given feature description and task, we need to analyze the relationship between the "job" feature and the target variable of whether the person receives a credit or not.

From prior knowledge, we can assume that the person's job can have an impact on their creditworthiness. Certain job positions might indicate stability and higher income, making it more likely for a person to receive a credit. On the other hand, job positions that indicate unemployment or low-income might decrease the likelihood of receiving a credit.

To analyze this relationship, we can group the data based on the target variable and then examine the distribution of different job categories within each group.

Here is the dictionary with the analysis:

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

Based on the analysis, it seems that individuals with skilled or high qualifications/self-employment/management jobs are more likely to receive a credit. On the other hand, individuals with unskilled resident or unemployed/unskilled non-resident jobs are less likely to receive a credit.

This analysis provides a general understanding of the relationship between the "job" feature and the target variable of receiving a credit. However, it is important to note that this is a simplified analysis and other factors could also influence the likelihood of receiving a credit.