Analysis:

Based on prior knowledge, it is reasonable to assume that age groups can generally be categorized into "Adult" and "Senior." To determine how the feature RIAGENDR (Respondent's Gender) relates to the target task of predicting the age group, we can analyze the distribution of gender values within each age group.

If we observe that a certain range of values for RIAGENDR is more prevalent in one age group than the other, we can infer a relationship between the feature and the target.

Possible analysis steps:
1. Group the data by age group (Adult vs. Senior).
2. Calculate the distribution of RIAGENDR values within each age group.
3. Identify any significant differences in the distribution of RIAGENDR values between age groups.
4. If there are distinct patterns, provide a list of typical RIAGENDR values for each age group.

Dictionary:

Based on the analysis, the dictionary can be structured as follows:

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

The values provided in the list represent typical RIAGENDR values for each age group. The values can be either 1.0 (representing Male) or 2.0 (representing Female).