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Exam 3: Forecasting

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Question 61

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Detecting nonrandomness in errors can be done using:

A)  MSEs.
B)  MAPs.
C)  control charts.
D)  correlation coefficients.
E)  strategies.

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

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Question 62

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The purpose of the forecast should be established first so that the level of detail, amount of resources, and accuracy level can be understood.

A) True
B) False

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Question 63

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Which technique is used in computing seasonal relatives?

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

The naive forecast is limited in its application to series that reflect no trend or seasonality.

A concert promoter is forecasting this year's attendance for one of his concerts based on the following historical data: $\begin{array} { | l | c | } \hline { \text { Year } } & \text { Attendance } \\\hline \text { Four Years ago } & 10,000 \\\hline \text { Three Years ago } & 12,000 \\\hline \text { Two Years ago } & 18,000 \\\hline \text { Last Year } & 20,000 \\\hline\end{array}$ The previous trend line had predicted 18,500 for two years ago, and 19,700 for last year. What was the mean absolute deviation for these forecasts?

Forecasts for groups of items tend to be less accurate than forecasts for individual items because forecasts for individual items don't include as many influencing factors.

When choosing a forecasting technique, a critical trade-off that must be considered is that between:

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 the least squares trend line for these data?

Accuracy in forecasting can be measured by:

The naive approach to forecasting requires a linear trend line.

The business analyst for Video Sales, Inc. wants to forecast this year's demand for DVD decoders based on the following historical data: $\begin{array} { | l | c | } \hline \text { Year } & \text { Enrollments } \\\hline \text { 5 Years ago } & 900 \\\hline \text { 4 Years ago } & 700 \\\hline \text { 3 Years ago } & 600 \\\hline 2 \text { Years ago } & 500 \\\hline \text { Last Year } & 300 \\\hline\end{array}$ What is the forecast for this year using a three-year weighted moving average with weights of .5, .3, and .2?

If a pattern appears when a dependent variable is plotted against time, one should use time series analysis instead of simple linear regression.

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 the least squares trend line for these data?

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

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

Once accepted by managers, forecasts should be held firm regardless of new input since many plans have been made using the original forecast.

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

In exponential smoothing, an alpha of 1.0 will generate the same forecast that a naive forecast would yield.

Minimizing the sum of the squared deviations around the line is called: