Based on the feature description, DIQ010 represents whether the respondent is diabetic or not. To analyze how this feature relates to the target variable (age group), we can consider common age-related factors for diabetes.

Typically, diabetes becomes more prevalent with increasing age. Therefore, it is reasonable to assume that the proportion of individuals with diabetes will be higher in the senior age group compared to the non-senior age group.

Based on this assumption, we can create a dictionary as follows:

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

In this example, we have assumed values of 0.0 and 1.0 to represent non-diabetic and diabetic, respectively. The values in the "Adult" list are for typical non-senior individuals, while the values in the "Senior" list are for typical senior individuals.

Please note that the actual values and distributions may vary depending on the dataset used and the context of the problem.