Based on prior knowledge, the feature "Insulin" which represents the -Hour serum insulin level (mu U/ml) can be used to analyze its relationship with the target variable "Does this person have diabetes?". 

To analyze the relationship, we need to consider typical ranges of values for insulin and observe how they correspond to the presence or absence of diabetes. In general, higher insulin levels are associated with diabetes, as the body may produce more insulin to compensate for insulin resistance. 

Here is the dictionary with the relationship between the feature "Insulin" and the target variable "Does this person have diabetes?".

```json
{
	"yes": [25.0, 60.5, 70.2, 80.0, 100.3],
	"no": [5.3, 15.2, 17.8, 20.6, 22.1]
}
```

In this dictionary, the "yes" key represents individuals who have diabetes, and the "no" key represents individuals who do not have diabetes. The associated values are typical insulin levels for each respective target class. The values are represented as floats in lists. Keep in mind that these values are just examples and can vary depending on the dataset or specific domain knowledge.