Based on prior knowledge, the lug_boot feature can have an influence on the rating of the decision to buy a car. A larger luggage boot may be seen as more favorable, as it provides more storage space and convenience. 

To create the required dictionary, we need to analyze the relationship between the lug_boot feature and the target variable, which is the rating of the decision to buy the car. We can examine the distribution of lug_boot values for each rating category to identify any patterns or trends.

After analyzing the relationship, the dictionary could be as follows:

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

In this analysis, it is observed that the car is not likely to receive an "unacceptable" rating if it has a medium or big luggage boot. However, there is a possibility that it could receive a lower rating if it has a small luggage boot. 

Cars with medium and big luggage boots are more likely to be rated as "good" or "very good". Cars with small luggage boots can still receive an "acceptable" rating, but are less likely to receive higher ratings.

Note: The exact distribution of lug_boot values for each category may vary depending on the dataset and specific analysis. This is a general analysis based on prior knowledge.