Based on the feature description and the task, we need to predict whether a person belongs to the senior age group or not based on their gender. 

To analyze the relationship between the feature (RIAGENDR) and the target (age group), we can examine the distribution of gender within each age group. However, it's important to note that age group ranges may vary depending on the context and data source. 

Based on common conventions, we can define the following age group ranges:

- Senior: typically considered as 65 years or older.
- Adult: typically considered as below 65 years.

Now let's generate the dictionary with the typical RIAGENDR values for each age group:

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

In this case, the values 1.0 represent males, and 2.0 represent females. The dictionary shows typical RIAGENDR values for each age group based on prior knowledge.