The company Handy Inc. produces a solar-powered electronic calculator that has experienced the following monthly sales history for the first four months of the year (in thousands of units): January February. March. April sales 23.3. 72.3. 30,3. 15.5 a) Find the forecast for May using the Moving Averages method with N = 3. b) Assume that the forecast for January was 25. Determine the one-step ahead forecast for February through May, using exponential smoothing with a smoothing constant of α = 0.15. c) Repeatthecalculationinproblembforavalueofα=0.40.
1.
The company Handy Inc. produces a solar-powered electronic calculator that has experienced the following monthly sales history for the first four months of the year (in thousands of units):
January February. March. April
sales 23.3. 72.3. 30,3. 15.5
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a) Find the forecast for May using the Moving Averages method with N = 3.
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b) Assume that the forecast for January was 25. Determine the one-step ahead forecast for February through May, using exponential smoothing with a smoothing constant of α = 0.15.
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c) Repeatthecalculationinproblembforavalueofα=0.40.
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d) Compare the MAD (Mean Absolute Deviation) and MSE (Mean Squared Error) for the forecast for February to April in problems b and c. Comment the accuracy of the forecasts.
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e) The demand in May turned out to be 30. Which of the three forecasting method were most accurate for this prediction?
2. Regression analysis
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Playstation 2 - Monthly sales 2002
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The figure shows sales figures in the first full sales year for the videogame console Nintendo “Gamecube”. As indicated by the figure sales showed increasing tendencies during the year 2002.
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a) Create a simple linear regression model, calculate parameters a and b and show the formula to be used for predicting “future” sales values. (Data for this case is available in Excel-format in Canvas. Only the data shown in the figure, i.e. from 2002, should be used for creating the model).
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b) Use the model to estimate the sales for the period 2003 – 2005. Compare your predictions with the actual sales data, and use the measure Mean Absolute Deviation (MAD) to show the forecast errors.
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c) What is the reason for the large deviation between predicted and actual sales? Do you have any suggestions for improvement of your model?
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