Based on prior knowledge, it is difficult to determine a direct relationship between the size of the luggage boot (lug_boot) and the decision to buy a car. However, we can still analyze the data to check for any patterns or trends.

To create the dictionary, we will need to examine the relationship between the lug_boot feature and the target variable "decision to buy this car". We can do this by analyzing the distribution of lug_boot values for each rating category.

Here is an example analysis of the relationship between the lug_boot feature and the target variable:

- Unacceptable: ['small', 'med', 'big'] (all possible values)
- Acceptable: ['small', 'med', 'big'] (all possible values)
- Very Good: ['med', 'big'] (based on the assumption that people looking for very good cars might want larger luggage spaces)
- Good: ['med', 'big'] (based on the assumption that people looking for good cars might want larger luggage spaces)

Based on this analysis, we can create the following dictionary:

```json
{
	"unacceptable": ["small", "med", "big"],
	"acceptable": ["small", "med", "big"],
	"very good": ["med", "big"],
	"good": ["med", "big"]
}
```

Please note that this analysis is based on assumptions and may vary depending on the specific dataset and task. It's important to perform further analysis and consider other features to make more accurate predictions.