Based on prior knowledge, we can analyze the relationship between the feature "Age" and the target variable "Does this person have diabetes?". 

Generally, age can be a factor in the development of diabetes as the risk increases with age. However, it is important to note that diabetes can occur at any age, including in children and young adults.

To analyze the relationship, we can consider typical age ranges for each target class: "yes" (person has diabetes) and "no" (person does not have diabetes).

Based on typical age ranges:

For the target class "yes" (person has diabetes):
- Possible values of feature "Age" can range from 30 to 70 years.
- Let's consider the following typical values: 35.5, 40.2, 45.8, 52.3, 67.9

For the target class "no" (person does not have diabetes):
- Possible values of feature "Age" can range from 20 to 60 years.
- Let's consider the following typical values: 23.1, 31.7, 43.9, 55.2, 60.6

Based on this analysis, we can generate the dictionary as follows:

```json
{
	"yes": [35.5, 40.2, 45.8, 52.3, 67.9],
	"no": [23.1, 31.7, 43.9, 55.2, 60.6]
}
```

Please note that these values are just hypothetical examples based on typical age ranges, and actual data may vary.