To analyze the relationship between the feature LBXGLU and the task of predicting the person's age group (senior or non-senior), we can use our prior knowledge of blood glucose levels.

Typically, higher blood glucose levels are associated with diabetes and insulin resistance, which are more common in older adults. Therefore, we can expect that higher blood glucose levels might indicate a higher likelihood of being a senior.

However, to determine the appropriate ranges of values for the feature LBXGLU, we need to take into account the specific context and data distribution of the dataset. Without access to the dataset or more information, it is challenging to accurately determine the precise ranges of values that would be appropriate to differentiate between senior and non-senior age groups.

Nevertheless, we can still create a dictionary with some example values for each target class based on our analysis:

```json
{
    "Adult": [85.2, 93.7, 102.3, 110.5, 118.9],
    "Senior": [112.1, 118.6, 125.4, 131.9, 137.5]
}
```

Please note that these values are just hypothetical examples and may not accurately represent the true relationship between LBXGLU and age groups in the given dataset. To obtain more accurate and reliable results, it is necessary to perform a thorough analysis on the actual dataset and determine the appropriate LBXGLU ranges based on its distribution.