Based on prior knowledge, we can assume that the target variable 'Senior' refers to individuals who are above a certain age threshold, typically 65 years or older. For this analysis, we will consider 'Senior' as those respondents who are 65 years or older and 'Adult' as those who are younger than 65. 

To analyze the relationship between the feature 'DIQ010' (if the respondent is diabetic) and the target variable, we will consider typical values for each target class.

```python
# Typical values of DIQ010 for the 'Adult' target class
DIQ010_Adult = [1.0, 2.0, 7.0, 9.0, 0.0]

# Typical values of DIQ010 for the 'Senior' target class
DIQ010_Senior = [1.0, 3.0, 2.0, 0.0, 9.0]
```

Here, we provide five typical values of 'DIQ010' for each target class. The values are represented as floats within lists.

And finally, here is the dictionary in the required format:

```json
{
	"Adult": [1.0, 2.0, 7.0, 9.0, 0.0],
	"Senior": [1.0, 3.0, 2.0, 0.0, 9.0]
}
```