The measure of forecast error where the amount of error of each forecast is squared and then an average is calculated is:
Understand the Problem
The question describes a specific method for measuring forecast error. It describes squaring the error for each estimate, then calculating an average of these squared errors. The goal is to correctly identify this particular error measurement method from the multiple choice answers.
Answer
Mean squared error (MSE).
The measure of forecast error where the amount of error of each forecast is squared and then an average is calculated is the mean squared error (MSE).
Answer for screen readers
The measure of forecast error where the amount of error of each forecast is squared and then an average is calculated is the mean squared error (MSE).
More Information
The mean squared error is a common metric used to evaluate the accuracy of forecasting models. Squaring the errors ensures that both positive and negative errors contribute to the overall error measure, and the averaging provides an overall sense of the magnitude of the errors.
Tips
A common mistake is confusing MSE with other error metrics like Mean Absolute Deviation (MAD) or Mean Absolute Percentage Error (MAPE). Remember that MSE specifically involves squaring the errors before averaging.
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