Based on prior knowledge, age can be an important factor in determining whether a person has diabetes or not. Let's analyze the relationship between age and the task of predicting whether a person has diabetes.

Analysis:
1. Younger individuals are less likely to have diabetes compared to older individuals. Therefore, for the target class 'yes' (person has diabetes), we can expect to see higher ages in general.

2. Older individuals are more likely to have diabetes compared to younger individuals. Thus, for the target class 'no' (person does not have diabetes), we can expect to see lower ages in general.

Based on this analysis, let's create the dictionary:

```json
{
	"yes": [45.2, 52.1, 49.8, 58.5, 63.2],
	"no": [31.7, 36.4, 41.9, 38.2, 44.6]
}
```

In this example, we have provided 5 typical age values for each target class ('yes' and 'no') as requested. These values are representative and can be used as examples of the relationship between age and the likelihood of having diabetes.