Based on my prior knowledge, I can analyze the relationship between the feature "RIAGENDR" (Respondent's Gender) and the task of predicting the person's age group. The feature "RIAGENDR" represents gender and has two possible values: 1 (Male) and 2 (Female). 

To determine the appropriate ranges of values for each target class, I will assume that "Adult" refers to individuals below a certain age threshold (e.g. 65 years) and "Senior" refers to individuals at or above this age threshold.

Based on this analysis, I can create the following dictionary:

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

Here, I have included 5 typical RIAGENDR values for each target class. For "Adult", the typical values are 1.0 (Male) and 2.0 (Female), indicating that both genders are represented in this age group. For "Senior", the typical values are 1.0 (Male) and 2.0 (Female), suggesting that both genders are also represented in this age group.

Please note that these values are based solely on assumptions and it may be necessary to perform a more detailed analysis or consult domain experts for a more accurate understanding of the relationship between the feature and target.