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# Analysis on 4 forecasting models using Excel

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Time Period Actual Number of Units Sold
1 33
2 36
3 32
4 35
5 33
6 36
7 34
8 38
9 37
10 36
11 38
12 38
13 37
14 39
15 35
16 38
17 37
18 39
19 37
20 35
21 37
22 34
23 35
24 36

1. Based upon using mean absolute deviation (MAD) as a measure of forecast accuracy, which of the forecast models would be the preferred forecast model (i.e., which model provides the greatest degree of forecasting accuracy)?
• 3-Month Moving Average Model
• 3-Month Weighted Moving Average Model
• Exponential Smoothing Model
• Regression Model

2. Based upon using mean squared error (MSE) as a measure of forecast accuracy, which of the forecast models would be the preferred forecast model (i.e., which model provides the greatest degree of forecasting accuracy)?
• 3-Month Moving Average Model
• 3-Month Weighted Moving Average Model
• Exponential Smoothing Model
• Regression Model

3. Based upon using mean absolute percent error (MAPE) as a measure of forecast accuracy, which of the forecast models would be the preferred forecast model (i.e., which model provides the greatest degree of forecasting accuracy)?
• 3-Month Moving Average Model
• 3-Month Weighted Moving Average Model
• Exponential Smoothing Model
• Regression Model

4. Based upon using mean absolute deviation (MAD) as a measure of forecast accuracy, which of the forecast models would be the least preferred forecast model (i.e., which model provides the greatest degree of forecasting inaccuracy)?
• 3-Month Moving Average Model
• 3-Month Weighted Moving Average Model
• Exponential Smoothing Model
• Regression Model

5. Based upon using mean squared error (MSE) as a measure of forecast accuracy, which of the forecast models would be the least preferred forecast model (i.e., which model provides the greatest degree of forecasting inaccuracy)?
• 3-Month Moving Average Model
• 3-Month Weighted Moving Average Model
• Exponential Smoothing Model
• Regression Model

6. Based upon using mean absolute percent error (MAPE) as a measure of forecast accuracy, which of the forecast models would be the least preferred forecast model (i.e., which model provides the greatest degree of forecasting inaccuracy)?
• 3-Month Moving Average Model
• 3-Month Weighted Moving Average Model
• Exponential Smoothing Model
• Regression Model

7. Based upon using the 3-Month Moving Average Model and mean absolute deviation (MAD) as a measure of forecast accuracy, what would be the interval estimate for projected demand for the test equipment for time period 25?
• 32.18 - 37.82
• 34.83 - 35.17
• 33.65 - 36.35
• 33.70 - 36.30

8. Based upon using the 3-Month Moving Average Model and mean squared error (MSE) as a measure of forecast accuracy, what would be the interval estimate for projected demand for the test equipment for time period 25?
• 34.83 - 35.17
• 33.65 - 36.35
• 33.70 - 36.30
• 32.18 - 37.82

9. Based upon using the 3-Month Moving Average Model and mean absolute percent error (MAPE) as a measure of forecast accuracy, what would be the interval estimate for projected demand for the test equipment for time period 25?
• 34.83 - 35.17
• 33.70 - 36.30
• 33.65 - 36.35
• 32.18 - 37.82

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#### Solution Summary

The solution gives detailed steps on comparing 4 forecasting models: MA, weighted MA, exponential and regression. I have done all analysis in excel.

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## Time series forecast

The U.S. Census Bureau publishes data on factory orders for all manufacturing, durable goods, and nondurable goods industries. Shown here are factory orders in the United States from 1987 through 1999 (\$ billion).
(a) Use these data to develop forecasts for the years 1992 through 2000 using a 4-year moving average.
(b) Use these data to develop forecasts for the years 1992 through 2000 using a 4-year weighted moving average. Weight the most recent year by 4, the previous year by 3, the year before that by 2, and the year before that by 1.
(c) Which method is more suitable for forecasting factory orders? Hint: Compare the two methods based on Mean Absolute Deviation (MAD)?

Year Factory Orders (\$ billion)
1987 2,512.7
1988 2,739.2
1989 2,874.9
1990 2,934.1
1991 2,865.7
1992 2,978.5
1993 3,092.4
1994 3,356.8
1995 3,607.6
1996 3,749.3
1997 3,952.0
1998 3,949.0
1999 4,137.0
(Black, Ken (2006). Business Statistics (4th ed. update). John Wiley & Sons, New York, NY. Page 615)

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Assignment Problem 2
The following table lists the worldwide shipments of personal computers (in thousands) according to Dataquest.

Year Shipments (in thousands)
1990 23,738
1991 26,966
1992 32,411
1993 38,851
1994 47,894
1995 60,171
1996 71,065
1997 82,400
1998 97,321
(a) Use exponential smoothing to determine the forecast of shipments for the year 1999. Use the actual shipments for 1990 as the starting forecast for 1991. Use a smoothing constant of ? = 0.4.
(b) Plot the data, fit a trend line, display equation and R2 on the chart and discuss the strength of prediction of the regression model. Note: Use 1990 = 1, 1991 = 2, and so on to get more accurate equation.
(c) Use the regression model to predict the shipments for the years 1992 through 1999.
(d) Which forecasting method would you prefer to use and why? Note: Compute MAPE for the two forecasting methods to compare the accuracy for the period 1992-1998.
(Adapted from: Black, Ken (2006). Business Statistics (4th ed. update). John Wiley & Sons, New York, NY. Page 622)

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