prepare a 65 word response to the following: The error metrics we used in this week's data exercise (MAD, MSE, and MAPE) measure how well the forecasting method can predict the historical values of the time series data to utilize it to predict future periods (In, 2020). While analyzing the error metrics (MAD, MSE, and MAPE), we can see that the two-period moving average is the best fit for the moving average models, and a smoothing constant of 0.6 is best for the exponential smoothing models. We can determine this by comparing each model's error metrics and seeing which has the lowest MAD, MSE, or MAPE. The two-period moving average has a lower MAD, MSE, and MAPE than the three and four-period methods. The smoothing constant 0.6 for exponential smoothing has the same MAPE as the 0.7 smoothing constant. However, the 0.6 smoothing constant has a lower MAD and MSE, making it the better forecasting method. If I had to choose a method, I would opt for the two-period moving average because it has the lowest error in forecasting, no matter which calculation was used (MAD, MSE, or MAPE). This means this forecasting method has less deviation from the actual units sold in this dataset. Since the two-period moving average model was a better fit than the three or four, that tells me that this data needed a model that can more quickly adjust to changes, thus only requiring two periods of data to make the most accurate forecast of units sold. I did not have any challenges with Excel this