Based on prior knowledge, we can analyze the relationship between the "duration" feature and the task of determining whether a person receives credit.

Typically, the duration of the credit would be an important factor in determining whether a person receives credit or not. Therefore, it is expected that the duration values for those who receive credit ("yes" class) and those who do not receive credit ("no" class) would differ.

To create the dictionary, we will need to analyze the relationship between the "duration" feature and the target variable. We can do this by looking at typical duration values for each target class.

Here is the dictionary with the desired format:

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

In this example, we have included 5 typical duration values for each target class. These values are [6.0, 12.0, 24.0, 36.0, 48.0] for the "yes" class (those who receive credit) and [6.0, 18.0, 30.0, 42.0, 60.0] for the "no" class (those who do not receive credit).

Note: The specific values mentioned above are just examples and can vary based on the dataset and domain knowledge.