Based on prior knowledge, there is a known association between age and the likelihood of developing diabetes. Generally, age is a risk factor for diabetes, with the risk increasing as one gets older. However, it is important to note that diabetes can also occur in younger individuals.

To analyze the relationship between age and the likelihood of having diabetes, we need data on whether each individual has diabetes or not. Based on this data, we can then identify typical age values for each target class.

Assuming we have the necessary data, here is an analysis and the accompanying dictionary:

```json
{
	"yes": [50.5, 55.2, 60.1, 65.7, 70.3], 
	"no": [35.9, 40.6, 45.2, 47.8, 52.4]
}
```

In this example, we have identified 5 typical age values for each class. The float values are representative ages for individuals who have diabetes (class "yes") and those who do not have diabetes (class "no").