Based on the given feature description, it appears that the SEQN represents the respondent sequence number. To determine the appropriate ranges of values for the feature SEQN, prior knowledge is required. Without this knowledge, it is difficult to draw any definitive conclusions about the relationship between SEQN and the target variable, which is an individual's age group (Senior or non-Senior).

However, assuming that the SEQN is a unique identifier for respondents, we can still generate a dictionary with values for each target class ['Adult', 'Senior']. Keep in mind that these value lists will not be representative of the actual relationship between SEQN and the target variable.

Here is an example of a dictionary you can use as a starting point:

```json
{
	"Adult": [1.0, 2.0, 3.0, 4.0, 5.0], 
	"Senior": [100.0, 101.0, 102.0, 103.0, 104.0]
}
```

Please note that the values in the dictionary above are just placeholders and do not represent any real relationship between SEQN and the target variable.