Based on prior knowledge, we can analyze the relationship between the feature "RIAGENDR" (Respondent's Gender) and the target variable of predicting age group (Senior or non-senior).

Typically, the age group "Adult" spans a wide range of ages, including young adults and middle-aged individuals. The age group "Senior" typically refers to older individuals.

To assess the relationship between the feature and target, we can analyze the distribution of gender within each age group and identify typical values for "RIAGENDR" for both the "Adult" and "Senior" groups.

Based on this analysis, we can create a dictionary:

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

In this example, the values for "RIAGENDR" (gender) in the "Adult" and "Senior" age groups are presented as floats in lists. The values "1.0" represent males, and "2.0" represents females.

Please note that the actual values in the lists may vary depending on the dataset and the specific distribution of gender within age groups.