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Actual sales of a company (in millions of dollars) for January through April are shown below. Month Sales January 17 February 26 March 31 April 39 May (a) Use a - 0.3 to compute the exponential smoothing values for sales. Compute MSE and forecast sales for May. Assume the forecast for the initial period (January) is 17. Show all of your computations from February through May. (Round your squared forecast error values and MSE to two decimal places.) Month Sales Squared Forecast Error Forecast January 17 February 26 March 31 April 39 May MSE (b) Use a = 0.1 to compute the exponential smoothing values for sales. Compute MSE and forecast sales for May. Assume the forecast for the initial period (January) is 17. Show all of your computations from February through May. (Round your squared forecast error values and MSE to two decimal places.) Month Sales Forecast Squared Forecast Error January 17 February 26 March 31 April 39 Мay MSE =

Actual sales of a company (in millions of dollars) for January through April are shown below. Month Sales January 17 February 26 March 31 April 39 May (a) Use a - 0.3 to compute the exponential smoothing values for sales. Compute MSE and forecast sales for May. Assume the forecast for the initial period (January) is 17. Show all of your computations from February through May. (Round your squared forecast error values and MSE to two decimal places.) Month Sales Squared Forecast Error Forecast January 17 February 26 March 31 April 39 May MSE (b) Use a = 0.1 to compute the exponential smoothing values for sales. Compute MSE and forecast sales for May. Assume the forecast for the initial period (January) is 17. Show all of your computations from February through May. (Round your squared forecast error values and MSE to two decimal places.) Month Sales Forecast Squared Forecast Error January 17 February 26 March 31 April 39 Мay MSE =

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Transcription for Educational Website: --- **Actual sales of a company (in millions of dollars) for January through April are shown below.** | Month | Sales | |-----------|-------| | January | 17 | | February | 26 | | March | 31 | | April | 39 | --- **(a)** Use \( \alpha = 0.3 \) to compute the exponential smoothing values for sales. Compute MSE and forecast sales for May. Assume the forecast for the initial period (January) is 17. Show all of your computations from February through May. (Round your squared forecast error values and MSE to two decimal places.) | Month | Sales | Forecast | Squared Forecast Error | |-----------|-------|----------|-------------------------| | January | 17 | | | | February | 26 | | | | March | 31 | | | | April | 39 | | | | May | | | | MSE = --- **(b)** Use \( \alpha = 0.1 \) to compute the exponential smoothing values for sales. Compute MSE and forecast sales for May. Assume the forecast for the initial period (January) is 17. Show all of your computations from February through May. (Round your squared forecast error values and MSE to two decimal places.) | Month | Sales | Forecast | Squared Forecast Error | |-----------|-------|----------|-------------------------| | January | 17 | | | | February | 26 | | | | March | 31 | | | | April | 39 | | | | May | | | | MSE = ---
Transcribed Image Text:Transcription for Educational Website: --- **Actual sales of a company (in millions of dollars) for January through April are shown below.** | Month | Sales | |-----------|-------| | January | 17 | | February | 26 | | March | 31 | | April | 39 | --- **(a)** Use \( \alpha = 0.3 \) to compute the exponential smoothing values for sales. Compute MSE and forecast sales for May. Assume the forecast for the initial period (January) is 17. Show all of your computations from February through May. (Round your squared forecast error values and MSE to two decimal places.) | Month | Sales | Forecast | Squared Forecast Error | |-----------|-------|----------|-------------------------| | January | 17 | | | | February | 26 | | | | March | 31 | | | | April | 39 | | | | May | | | | MSE = --- **(b)** Use \( \alpha = 0.1 \) to compute the exponential smoothing values for sales. Compute MSE and forecast sales for May. Assume the forecast for the initial period (January) is 17. Show all of your computations from February through May. (Round your squared forecast error values and MSE to two decimal places.) | Month | Sales | Forecast | Squared Forecast Error | |-----------|-------|----------|-------------------------| | January | 17 | | | | February | 26 | | | | March | 31 | | | | April | 39 | | | | May | | | | MSE = ---
(c) Based on MSE, which \( \alpha \) provides a better forecast? Explain why? - \( \circ \) The exponential smoothing using \( \alpha = 0.1 \) provides a better forecast since it has a smaller MSE than the exponential smoothing using \( \alpha = 0.3 \). - \( \circ \) The exponential smoothing using \( \alpha = 0.1 \) provides a better forecast since it has a larger MSE than the exponential smoothing using \( \alpha = 0.3 \). - \( \circ \) The exponential smoothing using \( \alpha = 0.3 \) provides a better forecast since it has a larger MSE than the exponential smoothing using \( \alpha = 0.1 \). - \( \circ \) The exponential smoothing using \( \alpha = 0.3 \) provides a better forecast since it has a smaller MSE than the exponential smoothing using \( \alpha = 0.1 \).
Transcribed Image Text:(c) Based on MSE, which \( \alpha \) provides a better forecast? Explain why? - \( \circ \) The exponential smoothing using \( \alpha = 0.1 \) provides a better forecast since it has a smaller MSE than the exponential smoothing using \( \alpha = 0.3 \). - \( \circ \) The exponential smoothing using \( \alpha = 0.1 \) provides a better forecast since it has a larger MSE than the exponential smoothing using \( \alpha = 0.3 \). - \( \circ \) The exponential smoothing using \( \alpha = 0.3 \) provides a better forecast since it has a larger MSE than the exponential smoothing using \( \alpha = 0.1 \). - \( \circ \) The exponential smoothing using \( \alpha = 0.3 \) provides a better forecast since it has a smaller MSE than the exponential smoothing using \( \alpha = 0.1 \).
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