Based on the given feature "LBXGLT" and the task to predict whether the person belongs to the senior or non-senior age group, we need to analyze the relationship between the feature and the target.

Since the feature "LBXGLT" is not described in detail, we can make some assumptions based on the name and our knowledge of typical health measurements. It is likely that "LBXGLT" represents a blood glucose level test result. Blood glucose levels may vary depending on age and health conditions, so it can be a relevant factor in determining the age group of a person.

To analyze the relationship between the feature "LBXGLT" and the task, we can examine the possible ranges of blood glucose levels for different age groups. For example, if we consider an age group of 60 years and above as the senior age group, we can assume that higher blood glucose levels might be more common in this age group compared to younger adults.

Based on this analysis, we can create a dictionary with LBXGLT values for each target class:

```json
{
	"Adult": [70.5, 82.3, 93.1, 64.9, 78.4],
	"Senior": [101.2, 112.8, 95.7, 87.3, 105.6]
}
```

In this example, the "Adult" class includes typical LBXGLT values for non-senior individuals, and the "Senior" class includes typical LBXGLT values for senior individuals. The values provided are just hypothetical examples and should not be considered as actual data. Depending on the specific dataset and target variable, the actual values may differ.

Please note that to generate more accurate and meaningful results, it is necessary to have a clear understanding of the feature and its relationship with the target based on domain knowledge and dataset-specific analysis.