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

We can categorize the age group as follows:
- Adult: Age below 65
- Senior: Age 65 and above

To analyze the relationship, we can consider the typical gender distributions within each age group. Let's assume that the target class 'Adult' refers to individuals below 65, and the target class 'Senior' refers to individuals 65 and above.

Based on this, we can create the following dictionary:

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

In this example, we assume that the RIAGENDR feature value 1.0 corresponds to males, and 2.0 corresponds to females. Hence, we randomly select 5 typical RIAGENDR values for each target class.