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Exam 3: Forecasting

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Question 61

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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: $\begin{array} { l l } \text { Week } & \text {\# Students } \\\hline 6 \text { weeks ago } & 83 \\5 \text { weeks ago } & 110 \\4 \text { weeks ago } & 95 \\3 \text { weeks ago } & 80 \\2 \text { weeks ago } & 65 \\\text { Last week } & 50\end{array}$ What is this week's forecast using trend-adjusted (double) smoothing with alpha = .5 and beta = .1, if the forecast for last week was 65, the forecast for two weeks ago was 75, and the trend estimate for last week's forecast was -5?

A) 49.3
B) 50.6
C) 51.3
D) 65.4
E) 78.7

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

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Question 62

Multiple Choice

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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 B)
G) None of the above

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Question 63

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A moving average forecast tends to be more responsive to changes in the data series when more data points are included in the average.

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

Which of the following mechanisms for enhancing profitability is most likely to result from improving short-term forecast performance?

The naive forecast can serve as a quick and easy standard of comparison against which to judge the cost and accuracy of other techniques.

Use linear regression to develop a predictive model for demand for burial vaults based on sales of caskets. $\begin{array} { l l l } \text { Year } & \text { Sales of Caskets } ( 000 ) & \text { Demand for Vaults } ( 000 ) \\\hline 1 & 8 & 5 \\2 & 7 & 2 \\3 & 10 & 6 \\4 & 6 & 4\end{array}$

Bias is measured by the cumulative sum of forecast errors.

Time-series techniques involve the identification of explanatory variables that can be used to predict future demand.

Suppose a three-period weighted average is being used to forecast demand. Weights for the periods are as follows: w_t-3 = 0.2, w_t-2 = 0.3 and w_t-1 = 0.5. Demand observed in the previous three periods was as follows: A_t-3 = 2,200, A_t-2 = 1,950, A_t-1 = 2,050. What will be the demand forecast for period t?

Given forecast errors of 4, 8, and -3, what is the mean absolute deviation?

A manager is using the equation below to forecast quarterly demand for a product: Y_t = 6,000 + 80t where t = 0 at Q2 of last year Quarter relatives are Q1 = .6, Q2 = .9, Q3 = 1.3, and Q4 = 1.2. What forecasts are appropriate for the last quarter of this year and the first quarter of next year?

Which is not a characteristic of simple moving averages applied to time series data?

Given the following historical data, what is the simple three-period moving average forecast for period 6? $\begin{array} { l l } \text { Period } & \text { Value } \\\hline 1 & 73 \\2 & 68 \\3 & 65 \\4 & 72 \\5 & 67\end{array}$

The more novel a new product or service design is, the more forecasters have to rely on:

In order to compute seasonal relatives, the trend of past data must be computed or known, which means that for brand-new products this approach cannot be used.

An advantage of a weighted moving average is that recent actual results can be given more importance than what occurred a while ago.

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 naive approach?

A consumer survey is an easy and sure way to obtain accurate input from future customers since most people enjoy participating in surveys.

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 a four-year simple moving average?