Based on prior knowledge, we can analyze the relationship between the `personal_status` feature and the task of whether the person receives credit or not.

Looking at the feature description, the possible values of `personal_status` are:
- "male single"
- "female div/dep/mar"
- "male div/sep"
- "male mar/wid"

To determine the relationship between `personal_status` and the target variable, we need to analyze the distribution of each value of `personal_status` among the two target classes (yes and no).

To create the dictionary, we will go through each value of `personal_status` and check for its occurrence in both target classes. Let's analyze this relationship:

- For the target class "yes":
    - "male single": This value may indicate that the person is young and possibly has no dependents. It is likely that this value would be present for individuals who receive credit.
    - "female div/dep/mar": It is difficult to predict the relationship between this value and the target class. We will exclude this value from the "yes" list.
    - "male div/sep": This value may indicate that the person is divorced or separated and thus may have financial instability, which could result in a higher chance of not receiving credit. We will exclude this value from the "yes" list.
    - "male mar/wid": This value may indicate that the person is married or widowed, which could imply stability and a higher likelihood of receiving credit. We will include this value in the "yes" list.

- For the target class "no":
    - "male single": This value may indicate that the person is young and possibly has no dependents. It is likely that this value would be present for individuals who do not receive credit.
    - "female div/dep/mar": This value may indicate that the person is divorced, dependent, or married, which could imply financial instability and a higher chance of not receiving credit. We will include this value in the "no" list.
    - "male div/sep": This value may indicate that the person is divorced or separated, which could imply financial instability and a higher likelihood of not receiving credit. We will include this value in the "no" list.
    - "male mar/wid": This value may indicate that the person is married or widowed, which could imply stability and a lower chance of not receiving credit. We will exclude this value from the "no" list.

Based on this analysis, the dictionary can be generated as follows:

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

Note: As mentioned earlier, for values that are difficult to predict, we exclude them from the respective target class list. Also, the inclusion or exclusion of values in the dictionary is based on general assumptions and might vary depending on the specific dataset and problem domain.