Based on the given feature description and task, we need to determine the appropriate ranges of values for the feature SEQN that can help predict a person's age group. 

Since the feature SEQN represents the respondent sequence number, it is difficult to directly analyze the relationship between SEQN and age group. It is likely that this feature does not have a direct relationship with the target variable, and as a result, we may not be able to identify specific values of SEQN that are indicative of an age group. 

However, we can still examine some typical values of SEQN for each target class to provide a general understanding of the feature. Let's assume that the age group classification is based on whether the person is above or below a certain age threshold, such as 65 years old. We can then consider the following dictionary:

```json
{
	"Adult": [100.0, 250.0, 500.0, 700.0, 900.0],
	"Senior": [2000.0, 2200.0, 2350.0, 2700.0, 3000.0]
}
```

Please note that these values are fictional and may not accurately reflect a true relationship between SEQN and age group. The values provided are only for illustration purposes and to demonstrate the format for the output dictionary.