Based on the given description, we are tasked with predicting whether a person belongs to the senior age group or not. The feature 'LBXGLT' represents the respondent's oral, and we need to determine the appropriate ranges of values for this feature.

To analyze the relationship between the feature 'LBXGLT' and the target variable 'age group', we can use our prior knowledge and assumptions about what might be considered typical for adults and seniors. 

Let's assume that the 'Adult' age group refers to individuals below the age of 60, and the 'Senior' age group includes individuals aged 60 and above. Based on this assumption, we can define the value ranges for 'LBXGLT' that are typical for each age group.

Assuming that 'LBXGLT' is a numeric variable, we can create a dictionary with the possible values of 'LBXGLT' for each target class ('Adult' and 'Senior'). Here's the dictionary:

```json
{
	"Adult": [25.0, 45.5, 37.8, 52.1, 30.2],
	"Senior": [42.6, 60.5, 68.9, 58.2, 51.7]
}
```

In this dictionary, I have included five typical values for each target class. The 'Adult' class includes values such as 25.0, 45.5, 37.8, 52.1, and 30.2, while the 'Senior' class includes values such as 42.6, 60.5, 68.9, 58.2, and 51.7.

Please note that the actual value ranges and typical values for 'LBXGLT' may vary based on the specific dataset and domain knowledge. This analysis is based on the assumption mentioned above.