Based on the feature description and the task at hand, it seems that SEQN is a numerical feature that represents the respondent sequence number. To predict the age group (senior or non-senior) of a person, we need to analyze the relationship between SEQN values and the corresponding age groups.

Since there is no further information provided about the ranges of values for SEQN, I will assume that the SEQN values can be considered unique identifiers for each respondent. In that case, it is not meaningful to analyze the relationship between SEQN values and age groups, as there is likely no inherent relationship between them.

However, to provide an example, let's create a sample dictionary with possible values for SEQN for the target classes 'Adult' and 'Senior', even though they may not be meaningful:

```json
{
	"Adult": [1.0, 3.0, 5.0, 7.0, 9.0],
	"Senior": [2.0, 4.0, 6.0, 8.0, 10.0]
}
```

Please note that these values are just placeholders and may not reflect any real relationship between SEQN and the age groups.