Based on prior knowledge, we can analyze the relationship between the feature "lug_boot" and the target variable "decision to buy this car". 

The size of the luggage boot may influence the decision to buy a car. Generally, people who need more space to store their belongings may prefer cars with bigger luggage boots. On the other hand, those who have less need for storage space may not prioritize the size of the luggage boot when making a purchasing decision.

To further analyze this relationship, we need to examine the distribution of the "lug_boot" feature values across the different ratings for the decision to buy the car.

Here is the analysis and the dictionary for the "lug_boot" feature:

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

Explanation:
- For cars that are rated as "unacceptable", the only possible value for the "lug_boot" feature is "small". This suggests that having a small luggage boot may contribute to a lower rating for buying the car.
- Cars rated as "acceptable" have a possible value of "med" for the "lug_boot" feature. This implies that having a medium-sized luggage boot may be considered acceptable by buyers.
- Cars with a rating of "very good" are associated with the value "big" for the "lug_boot" feature. This indicates that having a big luggage boot is highly desirable for buyers.
- Finally, cars rated as "good" may have either a "med" or "big" value for the "lug_boot" feature. This suggests that both medium and big luggage boots are seen as positive attributes for buyers, but it does not necessarily require a big luggage boot to receive a good rating.

Note: Since the "lug_boot" feature is a categorical variable with a limited number of values, it is not necessary to include values that do not occur or have a very low frequency in the dictionary. We only include the values that are relevant and have an observable relationship with the target variable.