Based on the given feature description (personal_status) and the task (Does this person receive a credit? Yes or no?), we can analyze the relationship between the feature and the target.

From the provided categories for personal_status: ['male single', 'female div/dep/mar', 'male div/sep', 'male mar/wid'], it seems that the feature captures the marital and gender status of the person. 

To analyze how personal_status relates to the target (credit received or not), we would need a dataset with labeled data to observe the correlation. Without the dataset, we can make some assumptions based on prior knowledge:

1. It is possible that single individuals (male or female) may have a higher chance of receiving credit, as there could be less financial responsibility or dependency on others.

2. Divorced, separated, or widowed individuals (male or female) may have a lower chance of receiving credit, as their marital status might indicate potential financial strains or instability.

Based on these assumptions, we can create the dictionary:

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

In this dictionary, we assume that "male single" and "female div/dep/mar" have a higher chance of receiving credit, while "male div/sep" and "male mar/wid" have a lower chance. Please note that this is a hypothetical analysis based on assumptions, and the actual relationship can only be known by analyzing the specific dataset.