Based on prior knowledge, age can be a useful predictor for the task of determining whether a person has diabetes or not. Typically, age is considered to be a risk factor for developing diabetes, with older individuals being more prone to the disease. 

To analyze the relationship between age and the target variable, we can look at the distribution of age values for both the "yes" and "no" classes. Based on this analysis, we can generate the requested dictionary. Here is the generated dictionary:

```json
{
	"yes": [45.0, 56.0, 62.0, 70.0, 51.0],
	"no": [35.0, 42.0, 29.0, 38.0, 47.0]
}
```

Note: The specific age values provided in the dictionary are arbitrary and can vary based on the dataset being analyzed.