Based on prior knowledge, the feature 'Insulin', which represents the -Hour serum insulin (mu U/ml), can be analyzed to determine its relationship with the target variable of whether a person has diabetes or not.

To generate the dictionary with specific details, we need to examine typical Insulin values for each target class of 'yes' and 'no'. Here is an analysis for the given task:

For individuals with diabetes (target class 'yes'), it is expected that the Insulin levels will typically be higher. Therefore, some typical Insulin values for this class could be:

['130', '140', '150', '160', '170']

On the other hand, for individuals without diabetes (target class 'no'), the Insulin levels are expected to be lower. Hence, some typical Insulin values for this class could be:

['50', '60', '70', '80', '90']

Based on this analysis, the dictionary can be generated as follows:

```json
{
	"yes": [130.0, 140.0, 150.0, 160.0, 170.0],
	"no": [50.0, 60.0, 70.0, 80.0, 90.0]
}
```

Note: The Insulin values in the dictionary are presented as float.