Based on the given feature description (SEQN), it appears to be a numeric variable representing a respondent sequence number. To analyze the relationship between SEQN and the target variable (age group), we can consider the typical values of SEQN for each target class.

Since there is no specific information about the age groups or the ranges of values for SEQN, we can only make general assumptions based on prior knowledge. Let's assume that the age group "Senior" refers to individuals who are 65 years or older, while the age group "Adult" refers to individuals below 65 years.

Using this assumption, we can create a dictionary with typical SEQN values for each target class ('Adult', 'Senior'):

```json
{
	"Adult": [10001.0, 20003.0, 30009.0, 40004.0, 50010.0],
	"Senior": [60001.0, 70005.0, 80007.0, 90002.0, 100008.0]
}
```

Please note that these values are randomly chosen examples and may not reflect the actual data distribution. It is recommended to use a real dataset with age group labels to perform a more accurate analysis.