Based on prior knowledge and assuming a credit usage decision is influenced by age, we can analyze the relationship between the "age" feature and the task of determining if a person receives a credit.

To conduct the analysis, we would typically use a dataset that contains information about individuals and whether they were granted credit or not. By analyzing this dataset, we can observe patterns and relationships between the "age" feature and the target variable.

However, as we don't have access to a dataset in this case, we can make some general assumptions based on common observations:

- Younger individuals may have a lower likelihood of receiving credit due to limited credit history or lack of stable income. Therefore, we can expect to find lower age values in the "no" category.
- Older individuals may have a higher likelihood of receiving credit due to more extensive credit history and potentially more stable income. Therefore, we can expect to find higher age values in the "yes" category.

Based on these assumptions, we can generate a dictionary with five typical age values for each target class:

```json
{
	"yes": [25.0, 30.0, 35.0, 40.0, 45.0],
	"no": [18.0, 20.0, 22.0, 24.0, 26.0]
}
```

Please note that these values are just examples and may not reflect the actual relationship between age and credit usage accurately without actual data analysis.