Based on the given feature description and task, we are trying to predict a person's age group (Senior or non-senior) based on the SEQN feature. 

To analyze the relationship between the SEQN feature and the age group, we need to understand the typical ranges of values for each target class. Without additional information, we can make an assumption that the SEQN feature represents a sequence number assigned to each respondent in a survey. 

Considering that senior citizens tend to have higher sequence numbers compared to non-senior adults, we can infer that higher values of SEQN may indicate senior age group. However, it's important to note that we do not have any specific ranges or thresholds to define "senior" and "adult" age groups, so we'll need to make some assumptions.

Based on this analysis, we can create a dictionary with possible values of the SEQN feature for each target class ('Adult' and 'Senior'), including 5 typical SEQN values for each:

```json
{
    "Adult": [100.0, 200.0, 300.0, 400.0, 500.0],
    "Senior": [1000.0, 1100.0, 1200.0, 1300.0, 1400.0]
}
```

Please note that the actual values may vary depending on the dataset and the specific context of the feature.