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Exam 4: Forecasting Demand

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Question 51

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Demand for individual products can be driven by product life cycles.

A) True
B) False

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Question 52

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A time-series model uses a series of past data points to make the forecast.

A) True
B) False

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Question 53

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A naïve forecast for September sales of a product would be equal to the forecast for August.

One use of short-range forecasts is to determine:

The last four weekly values of sales were 80,100,105,and 90 units.The last four forecasts were 60,80,95,and 75 units.These forecasts illustrate:

What is a time-series forecasting model?

________ is a forecasting technique based upon salespersons' estimates of expected sales.

________ expresses the error as a percent of the actual values.

When one constant is used to smooth the forecast average and a second constant is used to smooth the trend,the forecasting method is ________.

Regression lines graphically depict "cause-and-effect" relationships.

________ forecasts address the business cycle by predicting inflation rates,money supplies,housing starts,and other planning indicators.

The quarterly sales for specific educational software over the past three years are given in the following table.Compute the four seasonal factors.

Identify four quantitative forecasting methods.

Distinguish a dependent variable from an independent variable.

Describe the three forecasting time horizons and their use.

________ forecasting tries a variety of computer models and selects the best one for a particular application.

An innovative restaurateur owns and operates a dozen "Ultimate Low-Carb" restaurants in northern Arkansas.His signature item is a cheese-encrusted beef medallion wrapped in lettuce.Sales (x,in millions of dollars)is related to Profits (y-hat,in hundreds of thousands of dollars)by the regression equation y-hat = 8.21 + 0.76 x.What is your forecast of profit for a store with sales of $40 million? $50 million?

Distinguish between a weighted moving average model and an exponential smoothing model.

The two general approaches to forecasting are:

Cycles and random variations are both components of time series.