To analyze the relationship between the feature "LBXIN" (Respondent's Blood Insulin Levels) and the task of predicting the person's age group (Senior or non-senior), we need to determine the appropriate ranges of values for each age group.

Since the feature is the respondent's blood insulin levels, it is reasonable to assume that higher insulin levels may indicate a higher likelihood of being in the "Senior" age group. On the other hand, lower insulin levels may be more common in the "Adult" age group.

Based on this assumption, we can create a dictionary with typical LBXIN values for each target class as follows:

```json
{
	"Adult": [6.2, 7.4, 8.1, 9.3, 10.5],
	"Senior": [10.7, 12.2, 13.9, 15.1, 16.8]
}
```

These values are just examples and should be replaced with actual data analysis results. The specific values would depend on the dataset and the actual distribution of LBXIN values for each age group.