# Question With the gasoline time series data from the given table, show the exponential smoothing forecasts using = 0.1. Week Sales (1000s of gallons) 1 17 2 21 3 19 4 23 5 18 6 16 7 20 8 18 9 22 10 20 11 15 12 22 Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of = 0.1 or = 0.2 for the gasoline sales time series? Do not round your interim computations and round your final answers to two decimal places. = 0.1 = 0.2 MSE Prefer: 0.2 Are the results the same if you apply MAE as the measure of accuracy? Do not round your interim computations and round your final answers to two decimal places. = 0.1 = 0.2 MAE Prefer: 0.1 What are the results if MAPE is used? Do not round your interim computations and round your final answers to two decimal places. = 0.1 = 0.2 MAPE

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With the gasoline time series data from the given table, show the exponential smoothing forecasts using = 0.1.

 Week Sales (1000s of gallons) 1 17 2 21 3 19 4 23 5 18 6 16 7 20 8 18 9 22 10 20 11 15 12 22

1. Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of = 0.1 or = 0.2 for the gasoline sales time series? Do not round your interim computations and round your final answers to two decimal places.
 = 0.1 = 0.2 MSE
Prefer: 0.2
2. Are the results the same if you apply MAE as the measure of accuracy? Do not round your interim computations and round your final answers to two decimal places.
 = 0.1 = 0.2 MAE
Prefer: 0.1
3. What are the results if MAPE is used? Do not round your interim computations and round your final answers to two decimal places.
 = 0.1 = 0.2 MAPE
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Community Answer
WLE4DC

a. {:[14quad" Alpha "=0.1quad" Alpha "=0.2],[15" MSE "],[9.25],[8.98]:} Alpha = 0.2 ... See the full answer