Based on the given feature description and task, we need to analyze the relationship between the feature "personal_status" and the target variable "Does this person receive a credit?". 

From the feature description, it seems that "personal_status" includes different categories indicating marital and gender status. Without further information or context, it is difficult to precisely determine how this feature relates to the target variable. However, we can make some assumptions based on general knowledge.

Analyzing the relationship between the feature "personal_status" and the target variable, we can hypothesize that certain personal status categories might have a higher probability of receiving credit while others have a lower probability. For example, individuals who are married might have a higher chance of receiving credit compared to individuals who are single or divorced. However, it is also possible that these assumptions do not hold, as credit decisions can depend on a multitude of factors.

Based on this analysis, we can create a dictionary as requested:

```json
{
	"yes": ["female div/dep/mar", "male mar/wid"], 
	"no": ["male single", "male div/sep"]
}
```

In the above dictionary, we included the categories that are mentioned in the feature description. Since there are no specific guidelines for the "yes" or "no" categories, we have made an assumption that may not necessarily hold in practice. It is worth noting that this analysis and the resulting dictionary might vary depending on the specific dataset and context.