To analyze the relationship between the "personal_status" feature and the target variable "Does this person receive a credit?" (yes or no), we need to examine the distribution of personal status categories for each target class.

Here is the analysis:

- For the "yes" class (person receives a credit): 
  - From the given categories of personal_status, the possible values for the "yes" class are: 
    - ['female div/dep/mar', 'male mar/wid']
  - It seems reasonable to assume that individuals who are "female div/dep/mar" or "male mar/wid" are more likely to receive credit.
  
- For the "no" class (person does not receive a credit): 
  - From the given categories of personal_status, the possible values for the "no" class are: 
    - ['male single', 'male div/sep']
  - It seems reasonable to assume that individuals who are "male single" or "male div/sep" are more likely to not receive credit.

Based on this analysis, we can create the following dictionary:

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

Note: It is worth mentioning that this analysis is based on prior knowledge and assumptions. The actual relationship between the "personal_status" feature and the target variable may vary depending on the specific dataset and context.