Final answer:
The mean squared error (MSE) is the correct measure of the accuracy of a forecasting model. A variable is something whose value can change, and analytical modeling is the use of mathematical equations in modeling linear aspects of ecosystems. Therefore, the correct option is C.
Step-by-step explanation:
One measure of the accuracy of a forecasting model is the mean squared error (MSE). This statistical measure represents the average of the squares of the errors, which is the average squared difference between the estimated values and the actual value. MSE is a common measure to evaluate the performance of a predictive model, with lower values indicating a better fit between the model's predictions and the actual data. In the case described, where an economist is deriving a model to predict stock market outcomes, the MSE would help in assessing how well the model matched the actual index points recorded at the close of each day's trading.
Additionally, a variable is best described as something whose value can change over multiple measurements. In various situations like the temperature of a refrigerator fluctuating, the weight of a bag of rice, or the departure time of a commuter train, variables are the quantities that can vary from one instance to another.
In terms of statistical modeling, the use of mathematical equations to model linear aspects of ecosystems can be referred to as analytical modeling. This approach is used to represent the system with a set of equations, often simplifying complex behavior into linear relationships for analysis and prediction purposes.