Based on the description of the feature and task, it seems that the feature "SEQN" is a respondent sequence number and it is a numeric variable. The goal is to predict whether a person belongs to a senior or non-senior age group based on this feature.

Since the age group is not provided directly, we need to make some assumptions about the age ranges for seniors and non-seniors based on prior knowledge. Let's assume that seniors are individuals who are 60 years or older, and non-seniors are individuals younger than 60 years.

Now, let's analyze the relationship between the feature "SEQN" and the target variable (age group). To do this, we would need a dataset that includes both the "SEQN" values and the corresponding age group labels.

Once we have the dataset, we can separate the data into two groups based on the age group labels - one for seniors and one for non-seniors. Then, we can examine the distribution of "SEQN" values within each group to understand the relationship.

For the purpose of this exercise, I will assume hypothetical SEQN values that are evenly distributed within the range 1 to 1000 for both seniors and non-seniors, and create a dictionary based on this assumption. Please note that these values are not based on any actual data, and the relationship between SEQN and age group may vary in real-world scenarios.

```json
{
	"Adult": [1.0, 200.0, 400.0, 600.0, 800.0],
	"Senior": [100.0, 300.0, 500.0, 700.0, 900.0]
}
```

In this hypothetical scenario, we assume that SEQN values between 1 and 1000 are evenly distributed among seniors and non-seniors. The provided SEQN values (1.0, 200.0, 400.0, 600.0, 800.0) are typical for the non-senior group, while the SEQN values (100.0, 300.0, 500.0, 700.0, 900.0) are typical for the senior group.

Please note that in real-world scenarios, the relationship between SEQN and age group may be more complex and may require further analysis using actual data.