Based on the given feature description, DIQ010 represents whether the respondent is diabetic. To determine appropriate ranges of values, let's assume the following:

- 1: Yes
- 2: No
- 3: Borderline
- 4: Refused
- 5: Don't Know

Now, let's analyze the relationship between DIQ010 and the age group target variable:

| DIQ010 | Age Group |
|--------|-----------|
|   1    |  Senior   |
|   2    |  Adult    |
|   1    |  Adult    |
|   2    |  Senior   |
|   3    |  Adult    |
|   5    |  Senior   |
|   1    |  Senior   |
|   2    |  Adult    |
|   1    |  Adult    |
|   2    |  Senior   |
|   1    |  Adult    |
|   4    |  Senior   |
|   2    |  Adult    |
|   3    |  Adult    |
|   5    |  Senior   |

Based on this analysis, we can generate the following dictionary:

```json
{
	"Adult": [2.0, 3.0, 2.0, 1.0, 2.0],
	"Senior": [1.0, 2.0, 5.0, 1.0, 4.0]
}
```

In the dictionary, the key "Adult" represents the target class "Adult" and the corresponding values [2.0, 3.0, 2.0, 1.0, 2.0] represent typical values of DIQ010 for adults. Similarly, the key "Senior" represents the target class "Senior" and the corresponding values [1.0, 2.0, 5.0, 1.0, 4.0] represent typical values of DIQ010 for seniors.