Based on the given feature description (SEQN), it seems to be a respondent sequence number. 

To analyze the relationship between SEQN and the age group target variable, we can make some assumptions or estimates based on prior knowledge:

- Assuming SEQN is assigned in ascending order as people participate in the survey, higher SEQN values may correspond to younger individuals as they are enrolled later in the data collection process.
- Older individuals who are more likely to be seniors might have lower SEQN values as they are more likely to be among the first respondents.

However, it is important to note that without further information or context, the SEQN feature alone may not be directly related to age or age group. It represents the sequence of respondents in the dataset and does not provide explicit information about age.

Considering this, we can create a dictionary with random float values for SEQN for each target class:

```json
{
	"Adult": [1000.5, 1200.3, 1400.2, 2000.7, 2200.1],
	"Senior": [100.4, 300.8, 400.2, 500.1, 700.9]
}
```

Please note that the values provided above are randomly selected and do not represent any specific relationship between SEQN and the age group. They are included solely for demonstration purposes.