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Exam 3: Forecasting

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Question 41

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A manager uses this equation to predict demand: Y_t = 20 + 4t. Over the past eight periods, demand has been as follows. Are the results acceptable? Explain. $\begin{array} { l l l l l l l l l } \text { Period } ( \mathrm { t } ) & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 \\\hline \text { Demand } & 25 & 28 & 31 & 34 & 36 & 43 & 50 & 54\end{array}$

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\[\begin{array} { l r r r r r r r r }
\...

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Question 42

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Given an actual demand of 59, a previous forecast of 64, and an alpha of .3, what would the forecast for the next period be using simple exponential smoothing?

A) 36.9
B) 57.5
C) 60.5
D) 62.5
E) 65.5

F) B) and E)
G) A) and E)

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Question 43

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Trend-adjusted exponential smoothing uses double smoothing to add twice the forecast error to last period's actual demand.

The Delphi approach involves the use of a series of questionnaires to achieve a consensus forecast.

Over the past five years, a firm's sales have averaged 250 units in the first quarter of each year, 100 units in the second quarter, 150 units in the third quarter, and 300 units in the fourth quarter. What are appropriate quarter relatives for this firm's sales? Hint: Only minimal computations are necessary.

Forecasting techniques generally assume:

One reason for using the Delphi method in forecasting is to:

The two most important factors in choosing a forecasting technique are:

The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data: $\begin{array} { l l } \text { Year } & \text { Demand } \\\hline \text { 5 years ago } & 900 \\\text { 4 years ago } & 700 \\\text { 3 years ago } & 600 \\\text { 2 years ago } & 500 \\\text { Last year } & 300\end{array}$ What is the forecast for this year using trend-adjusted (double) smoothing with alpha = .3 and beta = .2, if the forecast for last year was 310, the forecast for two years ago was 430, and the trend estimate for last year's forecast was -150?

Data on demand over the last few years are available as follows: $\begin{array} { l l } \text { Time Period } & \text { Demand } \\\hline 7 \text { years ago } & 7 \\6 \text { years ago } & 28 \\\text { 5 years ago } & 21 \\\text { 4 years ago } & 42 \\\text { 3 years ago } & 35 \\\text { 2 years ago } & 56 \\\text { Last year } & 49\end{array}$ What are this and next year's forecasts using the least squares trend line for these data?

Data on demand over the last few years are available as follows: $\begin{array} { l l } \text { Time Period } & \text { Demand } \\\hline 7 \text { years ago } & 7 \\6 \text { years ago } & 28 \\\text { 5 years ago } & 21 \\\text { 4 years ago } & 42 \\\text { 3 years ago } & 35 \\\text { 2 years ago } & 56 \\\text { Last year } & 49\end{array}$ What is this year's forecast using exponential smoothing with alpha = .25, if last year's smoothed forecast was 45?

Forecasts based on judgment and opinion do not include:

A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data: $\begin{array} { l l } \text { Year } & \text { Attendance } \\\hline \text { Four years ago } & 10,000 \\\text { Three years ago } & 12,000 \\\text { Two years ago } & 18,000 \\\text { Last Year } & 20,000\end{array}$ What is this year's forecast using exponential smoothing with alpha = .2, if last year's smoothed forecast was 15,000?

The naive approach to forecasting requires a linear trend line.

The sample standard deviation of forecast error is equal to the square root of MSE.

Gradual, long-term movement in time series data is called:

The best forecast is not necessarily the most accurate.

For new products in a strong growth mode, a low alpha will minimize forecast errors when using exponential smoothing techniques.

The dean of a school of business is forecasting total student enrollment for this year's summer session classes based on the following historical data: $\begin{array} { l l } \text { Year } & \text { Enrollment } \\\hline \text { Four years ago } & 2000 \\\text { Three years ago } & 2200 \\\text { Two years ago } & 2800 \\\text { Last year } & 3000\end{array}$ What is this year's forecast using the least squares trend line for these data?

Seasonal relatives can be used to deseasonalize data or incorporate seasonality in a forecast.