Based on prior knowledge, we can analyze the relationship between the feature "RIAGENDR" (Respondent's Gender) and the target variable "age group". 

Since the task is to predict whether a person belongs to the "Adult" or "Senior" age group, we can make assumptions about the age ranges for each class. Let's assume that "Adults" are individuals aged 18 to 64, while "Seniors" are individuals aged 65 and above.

With this assumption, we can analyze the relationship between gender and age group. Here's the dictionary with the requested information:

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

The values for the "Adult" class (ages 18-64) include 1.0 (Male) and 2.0 (Female). The values for the "Senior" class (ages 65 and above) also include 1.0 (Male) and 2.0 (Female) to reflect that the same gender categories exist in both age groups. These values are taken as representative examples and can vary in the actual dataset.