Based on my prior knowledge, the duration feature is likely to have a relationship with whether a person receives credit or not. A longer duration may indicate a higher likelihood of receiving credit, as it implies the person has a longer credit history or a stable financial situation.

To conduct a thorough analysis, I will need the actual dataset. However, based on my assumptions, I can generate a dictionary with possible values of the duration feature for each target class:

```json
{
    "yes": [12.0, 24.0, 36.0, 48.0, 60.0],
    "no": [6.0, 18.0, 30.0, 42.0, 54.0]
}
```

In this example, the "yes" class represents individuals who receive credit, while the "no" class represents those who do not. The values provided in the dictionary are assumed typical duration values for each class. Keep in mind that this is a hypothetical analysis based on assumptions, and the actual relationship can only be confirmed by analyzing the specific dataset.