Based on the provided feature description of "personal_status" and the task of determining whether the person receives a credit or not, we can analyze the relationship between the feature and the target variable.

Given that the target variable is whether the person receives a credit or not, and the feature "personal_status" describes the personal status (married, single, etc.) and sex, it is reasonable to assume that personal status can have an impact on the credit decision.

To analyze the relationship between the feature and the target, we can consider the possible values of the feature and their distribution across the target variable. Let's go through each category of the "personal_status" feature and see how it relates to the target:

- "male single": This category represents single males. It is possible that single males have a higher likelihood of not receiving credit due to potentially higher financial risk or instability.

- "female div/dep/mar": This category represents females who are either divorced, separated, or dependents/married. Divorced or separated females may have a lower likelihood of receiving credit due to potential financial obligations related to their marital status. On the other hand, dependents and married females may have a higher likelihood of receiving credit due to potential stability and support.

- "male div/sep": This category represents males who are either divorced or separated. Similar to the "female div/dep/mar" category, divorced or separated males may have a lower likelihood of receiving credit due to financial obligations related to their marital status.

- "male mar/wid": This category represents males who are either married or widowed. Being married or widowed may indicate financial stability, which could lead to a higher likelihood of receiving credit.

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

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

Please note that the list of possible values for each target class may vary depending on the specific dataset and the distribution of the feature "personal_status".

It is important to note that this analysis is based on general assumptions and prior knowledge, and the actual relationship between the feature and the target variable should be validated using data analysis techniques, such as statistical testing or machine learning algorithms.