Based on the given feature "RIAGENDR" which represents the respondent's gender, we can analyze its relationship with the target variable "age group" to determine how it influences the prediction of whether a person is classified as a senior or non-senior.

Typically, the age range for being classified as a senior can vary depending on the context, but let's assume that 60 years and above would be considered as senior and anything below that as non-senior.

Based on this assumption, we can analyze the relationship between the values of "RIAGENDR" and the age groups:

- For the target class "Adult" (non-senior): The possible values of "RIAGENDR" could be both 1 (male) and 2 (female).

- For the target class "Senior": The possible values of "RIAGENDR" could be both 1 (male) and 2 (female).

Please note that these are just assumptions based on common knowledge and more specific context might alter these ranges.

Now let's create the dictionary with typical "RIAGENDR" values for each target class:

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

In the above dictionary, 1.0 represents "male" and 2.0 represents "female". The values provided are just examples and can vary based on the dataset being used for analysis.