To analyze the relationship between the duration feature and the task of predicting whether a person receives credit or not, we can consider the following:

1. Previous knowledge suggests that the duration of a credit can often be an important factor in determining whether a person receives credit or not. Generally, shorter durations are associated with a higher likelihood of receiving credit.

Based on this analysis, we can create a dictionary as shown below:

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

For the target class "yes" (person receives credit), we have included 5 typical duration values: 6.0, 12.0, 18.0, 24.0, and 30.0. These values represent relatively short durations that are common for individuals who receive credit.

For the target class "no" (person does not receive credit), we have included 5 typical duration values: 30.0, 36.0, 42.0, 48.0, and 54.0. These values represent relatively longer durations that are often associated with individuals who do not receive credit.

Please note that the actual values in the lists might vary based on the specific dataset and the definition of duration ranges. This analysis and dictionary are based on general knowledge and assumptions.