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Exam 3: Forecasting

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Question 31

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In order to update a moving average forecast, the values of each data point in the average must be known.

A) True
B) False

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Question 32

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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 exponential smoothing with alpha = .4, if the forecast for two years ago was 750?

A) 163
B) 180
C) 300
D) 420
E) 510

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

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Question 33

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The T in the model TAF = S + T represents the time dimension (which is usually expressed in weeks or months).

A manager has been using a certain technique to forecast demand for gallons of ice cream for the past six periods. Actual and predicted amounts are shown below. Would a naive forecast have produced better results? $\begin{array} { l l l } \text { Period } & \text { Demand } & \text { Forecast } \\\hline 1 & 90 & 87 \\2 & 85 & 88 \\3 & 91 & 87 \\4 & 92 & 89 \\5 & 95 & 90 \\6 & 88 & 92\end{array}$

A managerial approach toward forecasting which seeks to actively influence demand is:

Customer service levels can be improved by better:

Which of the following is not a type of judgmental forecasting?

Given the following data, develop a linear regression model for y as a function of x. $\begin{array} { l l } x & y \\\hline 8 & 10 \\4 & 5 \\10 & 15 \\15 & 20 \\7 & 10\end{array}$

Forecasts of future demand are used by operations people to plan capacity.

A smoothing constant of .1 will cause an exponential smoothing forecast to react more quickly to a sudden change than a smoothing constant value of .3.

The president of State University wants to forecast student enrollments for this academic year based on the following historical data: $\begin{array} { l l } \text { Year } & \text { Enrollments } \\\hline \text { 5 years ago } & 15,000 \\4 \text { years ago } & 16,000 \\\text { 3 years ago } & 18,000 \\2 \text { years ago } & 20,000 \\\text { Last year } & 21,000\end{array}$ What is the forecast for this year using the naive approach?

A CPA firm has been using the following equation to predict annual demand for tax audits: Y_t = 55 + 4t. Demand for the past few years is shown below. Is the forecast performing as well as it might? Explain. $\begin{array} { l l l l c l l } \text { Year } & 2 & 3 & 4 & 5 & 6 & 7 \\\hline \text { Demand } & 60 & 65 & 69 & 76 & 85 & 85 \\\end{array}$ $\begin{array} { l l l l c l l } \mathrm { t } & \text { Demand } & \text { Forecast } & \mathrm { D } - \mathrm { F } & \mathrm { e } ^ { 2 } & \\\hline 2 & 60 & 63 &\ - 3 & 9 & \\3 & 65 & 67 & - 2 & 4 & \\4 & 69 & 71 & - 2 & 2 & \\5 & 76 & 75 & + 1 & 1 & \\6 & 85 & 79 & + 6 & 36 & \\7 & 85 & 83 & + 2 & 4 &\end{array}$

Forecasts based on an average tend to exhibit less variability than the original data.

The primary difference between seasonality and cycles is:

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 a three-year simple moving average?

Trend-adjusted exponential smoothing requires selection of two smoothing constants.

When new products or services are introduced, focus forecasting models are an attractive option.

A persistent tendency for forecasts to be greater than or less than the actual values is called:

Demand for the last four months was: $\begin{array} { l l l l l } \text { Month } & \text { March } & \text { April } & \text { May } & \text { June } \\\hline \text { Demand } & 6 & 8 & 10 & 8\end{array}$ A) Predict demand for July using each of these methods: 1) a three-period moving average 2) exponential smoothing with alpha equal to .20 (use a naive forecast for April for your first forecast) B) If the naive approach had been used to predict demand for April through June, what would MAD have been for those months?

Curvilinear and multiple regression procedures permit us to extend associative models to relationships that are nonlinear or involve more than one predictor variable.