Without the actual linear regression equation, which includes the slope and y-intercept, it is not possible to predict the sales revenue of rock salt for 11 snow days based on the provided data from the store manager.
To predict the sales revenue of rock salt given there were 11 snow days in the month using a linear best-fit model, we first need the equation of the line that best fits the given data points. The data provided in the question is insufficient to derive such a model directly, as it does not include a specific regression equation. However, a typical approach to finding the equation for the best-fit line involves calculating the slope and y-intercept based on the data, using methods such as the least-squares method.
Once the linear equation is determined, it will have a general form of ÿd = mx + b, where ÿd represents the predicted sales revenue, m is the slope, x is the number of snow days, and b is the y-intercept. To make a prediction for 11 snow days, you would substitute x with 11 in the equation, and calculate the corresponding ÿd (predicted sales revenue).
Given that we do not have the actual slope and y-intercept from the question, the provided answer choices cannot be justified without additional information. Therefore, unfortunately, I cannot provide a predicted value for the sales revenue of rock salt for 11 snow days without the regression equation.