Based on prior knowledge, it is known that high levels of glucose can be indicative of diabetes. Therefore, analyzing the relationship between the feature "Glucose" and the task "Does this person have diabetes?" can help determine the appropriate ranges of values.

To analyze the relationship, we can examine the distributions of the feature values for each target class ('yes' and 'no'). This will provide insight into typical glucose values for individuals with and without diabetes.

Here is a dictionary with the requested information:

```json
{
	"yes": [180.0, 160.0, 200.0, 190.0, 175.0],
	"no": [85.0, 95.0, 105.0, 90.0, 80.0]
}
```

The values provided above are examples and can vary depending on the dataset being analyzed. These values represent typical glucose values for individuals with and without diabetes.