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Exam 3: Forecasting

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Question 1

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For the data given below, what would the naive forecast be for period 5? $\begin{array} { | c | c | } \hline \text { Period } & \text { Value } \\\hline 1 & 58 \\\hline 2 & 59 \\\hline 3 & 60 \\\hline 4 & 61 \\\hline\end{array}$

A)  58
B)  62
C)  59.5
D)  61
E)  cannot tell from the data given

F) A) and C)
G) C) and E)

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Question 2

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The forecasting method which uses anonymous questionnaires to achieve a consensus forecast is:

A)  sales force opinions.
B)  consumer surveys.
C)  the Delphi method.
D)  time series analysis.
E)  executive opinions.

F) A) and D)
G) None of the above

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Question 3

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The two general approaches to forecasting are:

Given the following historical data, what is the simple three-period moving average forecast for period 6? $\begin{array} { | l | c | } \hline { \text { Year } } & \text { Attendance } \\\hline \text { Four Years ago } & 10,000 \\\hline \text { Three Years ago } & 12,000 \\\hline \text { Two Years ago } & 18,000 \\\hline \text { Last Year } & 20,000 \\\hline\end{array}$

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

An advantage of trend-adjusted exponential smoothing over the linear trend equation is its ability to adjust over time to changes in the trend.

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

MAD is equal to the square root of MSE, which is why we calculate the easier MSE and then calculate the more difficult MAD.

A seasonal relative (or seasonal indexes) is expressed as a percentage of average or trend.

In order to increase the responsiveness of a forecast made using the moving average technique, the number of data points in the average should be:

Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data: What is this week's forecast using a three-week simple moving average

Customer service levels can be improved by better:

The president of State University wants to forecast student enrollments for this academic year based on the following historical data: $\begin{array} { | l | c | } \hline \text { Year } & \text { Enrollments } \\\hline \text { 5 Years ago } & 15,000 \\\hline \text { 4 Years ago } & 16,000 \\\hline \text { 3 Years ago } & 18,000 \\\hline \text { 2 Years ago } & 20,000 \\\hline \text { Last Year } & 21,000 \\\hline\end{array}$ What is the forecast for this year using exponential smoothing with alpha = .5, if the forecast for two years ago was 16,000?

A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data: $\begin{array} { | l | c | } \hline { \text { Year } } & \text { Attendance } \\\hline \text { Four Years ago } & 10,000 \\\hline \text { Three Years ago } & 12,000 \\\hline \text { Two Years ago } & 18,000 \\\hline \text { Last Year } & 20,000 \\\hline\end{array}$ What is this year's forecast using a two-year weighted moving average with weights of .7 and .3?

The owner of Darkest Tans Unlimited in a local mall is forecasting this month's (October's) demand for the one new tanning booth based on the following historical data: $\begin{array} { | l | c | } \hline { \text { Month } } & \text { Number of Visits } \\\hline \text { April } & 100 \\\hline \text { May } & 140 \\\hline \text { June } & 110 \\\hline \text { July } & 150 \\\hline \text { August } & 120 \\\hline \text { September } & 160 \\\hline\end{array}$ What is this month's forecast using exponential smoothing with alpha = .2, if August's forecast was 145?

Which term most closely relates to associative forecasting techniques?

Putting forecast errors into perspective is best done using

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?

Forecasting techniques that are based on time-series data assume that future values of the series will duplicate past values.

Which of the following is the most valuable piece of information the sales force can bring into forecasting situations?