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

From experience, we can loosely assume that the size of the luggage boot (lug_boot) can have an impact on the decision to buy a car. People with a higher need for luggage space may prefer larger boot sizes, while those who don't require much storage may not have a strong preference regarding lug_boot.

Using this assumption, we can analyze the relationship between these variables. We need to consider the possible values of the lug_boot feature and their distributions across the target variable classes (unacceptable, acceptable, good, and very good).

Here is the dictionary with the analysis:

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

Explanation:
- For the target class "unacceptable", the only possible value of the lug_boot feature is "small".
- For the target class "acceptable", the possible values of the lug_boot feature are "small" and "med".
- For the target class "very good", the only possible value of the lug_boot feature is "big".
- For the target class "good", the possible values of the lug_boot feature are "med" and "big".

Please note that the dictionary only includes the possible values of lug_boot for each target class, based on the assumption that the size of the luggage boot might have an impact on the decision to buy the car.