Based on my prior knowledge, I can analyze the relationship between the "personal_status" feature and the task of whether a person receives a credit or not. 

Looking at the feature description, it seems that the personal_status feature includes the marital status and sex of the person. 

To generate the required dictionary, I will analyze each category of the personal_status feature and determine whether it is likely to be associated with a "yes" or "no" target class.

Based on the provided categories, here is the analysis:

- For the "yes" target class:
    - Possible values for personal_status may include:
        - 'female div/dep/mar': This category indicates that the person is either divorced, separated, or married. It is possible that individuals with this status may be more likely to receive a credit. Therefore, we can include this value in the "yes" category.
    
- For the "no" target class:
    - Possible values for personal_status may include:
        - 'male single': This category suggests that the person is a single male. It is possible that single males may be less likely to receive a credit. Therefore, we can include this value in the "no" category.
        - 'male div/sep': This category indicates that the person is a divorced or separated male. Similar to the "male single" category, it is possible that males with these marital statuses may have a lower chance of receiving a credit. Therefore, we can include this value in the "no" category.
        - 'male mar/wid': This category suggests that the person is a married or widowed male. Similar to the previous categories, individuals in this category may also be less likely to receive a credit. Therefore, we can include this value in the "no" category.

Based on the analysis, here is the required dictionary:

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