Based on the given feature description, SEQN is a numeric variable that represents the respondent sequence number. In order to determine the appropriate ranges of values for predicting the target variable, we need to consider the typical age groups for adults and seniors.

Before proceeding, it is important to note that the given feature description does not directly provide information about the respondent's age. To make accurate predictions about age groups, additional features related to age, such as date of birth, would be necessary.

However, assuming that SEQN is somehow related to age and that we have prior knowledge about typical age ranges for adults and seniors, we can still perform an analysis based on this assumption.

An example dictionary, with dummy values, based on this assumption, could look like the following:

```json
{
	"Adult": [1000.0, 2000.0, 3000.0, 4000.0, 5000.0],
	"Senior": [6000.0, 7000.0, 8000.0, 9000.0, 10000.0]
}
```
In this example, the SEQN values for the "Adult" class are [1000.0, 2000.0, 3000.0, 4000.0, 5000.0], and the SEQN values for the "Senior" class are [6000.0, 7000.0, 8000.0, 9000.0, 10000.0]. These values are just placeholders and do not represent any specific age ranges or patterns.

Please note that without additional information and without knowing the underlying data, it is not possible to accurately determine the appropriate ranges of values for age groups solely based on the given feature description. The provided analysis is purely hypothetical and assumes a relationship between SEQN and age groups.