Final answer:
The coefficient of the variable "Italian" indicates that on average, the customer satisfaction score will increase by 3 points for every one-unit increase in the "Italian" variable, given that the average meal price remains constant.
Step-by-step explanation:
The interpretation of the coefficient of the variable "Italian" is crucial in understanding how the variable affects the average customer satisfaction score. When analyzing data and determining how an independent variable affects a dependent variable, the coefficient represents the average change in the dependent variable for a one-unit increase in the independent variable, keeping all other variables constant.
In this context, the correct interpretation among the given options is:
a. The average customer satisfaction score will increase by 3 points for a one-unit increase in the variable "Italian," holding the average meal price constant.
This implies that if the variable "Italian" is coding for the presence of Italian restaurants in a linear model, their presence is associated with an average increase of 3 points in customer satisfaction scores in comparison to the reference category, which could be seafood/steakhouse restaurants, assuming all other factors in the model are held constant. It's important to understand that this does not indicate causation, but rather an association captured by the model.