Based on the given feature description, the feature SEQN represents the respondent sequence number. To determine the appropriate ranges of values, we need to analyze the relationship between the SEQN feature and the target variable (age group). Since the task is to predict whether a person belongs to the senior or non-senior age group, we can assume that the SEQN feature is not directly related to age.

Without any additional information about the dataset or the target variable, it is challenging to analyze the exact relationship between the SEQN feature and age group. However, we can assume that the SEQN feature represents a unique identifier for each respondent and is unlikely to have a significant relationship with age.

As a result, it wouldn't be appropriate to analyze the possible values of the SEQN feature for each target class (Adult and Senior) since the feature is not directly related to age.

However, to complete the requested dictionary format, we can randomly choose some numerical values as placeholders. Keep in mind that these values do not represent a meaningful relationship between the SEQN feature and age group:

```json
{
	"Adult": [100.0, 200.0, 300.0, 400.0, 500.0],
	"Senior": [1000.0, 2000.0, 3000.0, 4000.0, 5000.0]
}
```

Please note that these values are arbitrary and not based on any analysis. Without further context or information, it is not possible to provide meaningful and relevant SEQN values related to age groups.