Based on prior knowledge, we can analyze the relationship between the feature DIQ010 (diabetic status) and the task of predicting the person's age group.

Since the task is to predict if the person is a senior or non-senior, we can consider individuals aged 65 and above as seniors and those below 65 as non-seniors. 

To create the dictionary, we will identify typical DIQ010 values for each target class (Adult and Senior). 

Here is the analysis and corresponding dictionary:

```json
{
	"Adult": [0.0, 1.0, 2.0, 3.0, 7.0],  
	"Senior": [0.0, 1.0, 2.0, 3.0, 7.0]  
}
```

In this case, we assume that there are no specific ranges of values for the feature DIQ010 that are indicative of being a senior or non-senior. Therefore, we include the same typical values for both target classes.