Based on the feature description, we have a numeric variable RIAGENDR representing the respondent's gender. The values 1 and 2 correspond to male and female, respectively.

To analyze the relationship between gender and age group, we need to inspect how the feature varies across the target classes "Adult" and "Senior".

Here is an analysis of the relationship between the feature RIAGENDR and the task of predicting age group:

- Adult: In this target class, we expect to see a mix of both males and females across the dataset. Hence, we can assume that the values of RIAGENDR for the "Adult" class will be a combination of 1's and 2's.

- Senior: In this target class, we might expect to see a higher proportion of females compared to males. This assumption is primarily based on the fact that in many populations, women tend to have a longer life expectancy compared to men. However, without specific data or prior knowledge about the dataset, we cannot say for certain if there is a correlation between gender and age group.

With this information, we can now generate a dictionary that includes typical values of RIAGENDR for each target class:

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

Please note that the values provided in the dictionary are just examples and may vary based on the specific dataset.