Question

A forecaster would like to use the 2-period moving average method on data below to make forecasts. Before doing this, she would like to test the performance of this method on past data. Which of the following could represent past forecasts for this method? (In the options below note that F1 represents the forecast for time period 1, and so on.)

Period	1	2	3	4	5
Actual Sales	5582	5122	5755	6320	5153

	A.	F2 = 5352       F3=5352            F4 = 5348.5       F5 = 6037.5
	B.	F1 = 5586.4     F2 = 5586.4    F3 = 5586.4       F4 = 5586.4      F5 = 5586.4
	C.	F1 = 5582        F2 = 5352      F3 = 5438.5      F4 = 6037.5       F5 = 5736.5
	D.	F3=5352            F4 = 5438.5       F5 = 6037.5

1. The sales at a retail store has shown to fluctuate over the different quarters of the year. To predict sales, the following regression model has been fitted to past data in which the variables “summer”, “fall” and “winter” are dummy variables representing which season each quarter belongs to:

     Sales = 27.5 + 0.85 * # of quarters + 0.3 * summer + 0.1 * fall – 0.5 * winter.

   Which of the following represents the forecast for quarter 25, which coincides with a “spring” season?

   	A.	48.75
   	B.	28.25
   	C.	46.25
   	D.	27.5

1. The Music Company has been in business for three years. During this time the sale of electric organ has grown. The owner would like to forecast quarterly sales for the next year.

   Quarter	Year 1	Year 2	Year 3
   Winter	10	12	18
   Spring	3	9	10
   Summer	5	7	13
   Fall	16	22	35

   Using Seasonal (additive regression) Model with trend, the MAE (the Mean Absolute Error) is.

   	A.	1.97
   	B.	1.06
   	C.	1.40
   	D.	can't be determined

Considering the models analyzed above with the Music company case, which model would you use for forecasting

	A.	none of the other choices
	B.	4 period moving average
	C.	Exponential smoothing with a = 0.3
	D.	Seasonal Model

Answer 1

A forecaster would like to use the 2-period moving average method on data below to make forecasts. Before doing this, she would like to test the performance of this method on past data. Which of the following could represent past forecasts for this method? (In the options below note that F1 represents the forecast for time period 1, and so on.)

D. F3=5352            F4 = 5438.5       F5 = 6037.5

Data			Forecasts and Error Analysis
Period	Demand		Forecast	Error	Absolute	Squared	Abs Pct Err
Period 1	5582
Period 2	5122
Period 3	5755		5352	403	403	162409	07.00%
Period 4	6320		5438.5	881.5	881.5	777042.3	13.95%
Period 5	5153		6037.5	-884.5	884.5	782340.3	17.16%
			Total	400	2169	1721792	38.12%
			Average	133.3333	723	573930.5	12.71%
				Bias	MAD	MSE	MAPE
					SE	1312.171
Next period	5736.5

The sales at a retail store has shown to fluctuate over the different quarters of the year. To predict sales, the following regression model has been fitted to past data in which the variables “summer”, “fall” and “winter” are dummy variables representing which season each quarter belongs to:

  Sales = 27.5 + 0.85 * # of quarters + 0.3 * summer + 0.1 * fall – 0.5 * winter.

Which of the following represents the forecast for quarter 25, which coincides with a “spring” season?

A. 48.75

The Music Company has been in business for three years. During this time the sale of electric organ has grown. The owner would like to forecast quarterly sales for the next year.

Quarter	Year 1	Year 2	Year 3
Winter	10	12	18
Spring	3	9	10
Summer	5	7	13
Fall	16	22	35

Using Seasonal (additive regression) Model with trend, the MAE (the Mean Absolute Error) is.

A. 1.97

Considering the models analyzed above with the Music company case, which model would you use for forecasting

D. Seasonal Model