# 3306 3

A. Trend

B. Seasonal

C. Cyclical

D. Variance

E. Autocorrelation

A. Qualitative

B. Time series analysis

C. Causal relationships

D. Simulation

E. Force field analysis

A. Average demand for a period

B. A trend

C. Seasonal elements

D. Past data

E. Autocorrelation

A. Cyclical elements

B. Future demand

C. Past demand

D. Inconsistent demand

E. Level demand

A. Forecast error

B. Autocorrelation

C. Previous demand

D. Consistent demand

E. Repeat demand

A. Simple moving average

B. Market research

C. Linear regression

D. Exponential smoothing

E. Multiple regression

A. Simple moving average

B. Market research

C. Leading indicators

D. Historical analogy

E. Simulation

A. Delphi method

B. Exponential averaging

C. Simple movement smoothing

D. Weighted moving average

E. Simulation

A. Exponential smoothing

B. Weighted moving average

C. Linear regression

D. Historical analogy

E. Market research

A. Historical analogy

B. Time series analysis

C. Panel consensus

D. Market research

E. Linear regression

A. Time series analysis

B. Simple moving average

C. Weighted moving average

D. Delphi method

E. Panel consensus

A. Four weeks or less

B. More than three months

C. Six months or more

D. Less than three months

E. One year

A. Six weeks to one year

B. Three months to two years

C. One to five years

D. One to six months

E. Six months to six years

A. Three months or longer

B. Six months or longer

C. One year or longer

D. Two years or longer

E. Ten years or longer

A. Short-term forecasts

B. Quick-time forecasts

C. Long range forecasts

D. Medium term forecasts

E. Rapid change forecasts

A. Short-term forecasts

B. Quick-time forecasts

C. Long range forecasts

D. Medium term forecasts

E. Rapid change forecasts

A. Short-term forecasts

B. Quick-time forecasts

C. Long range forecasts

D. Medium term forecasts

E. Rapid change forecasts

A. Simple exponential smoothing

B. Delphi technique

C. Market research

D. Hoskins-Hamilton smoothing

E. Serial regression

A. Time horizon to forecast

B. Product

C. Accuracy required

D. Data availability

E. Analyst availability

A. The most recent forecast

B. Precise actual demand for the past several years

C. The value of the smoothing constant delta

D. Overall industry demand data

E. Tracking values

A. 5 % to 10 %

B. 20 % to 50 %

C. 20 % to 80 %

D. 60 % to 120 %

E. 90 % to 100 %

A. Close to zero

B. A very low percentage, less than 10%

C. The more rapid the growth, the higher the percentage

D. The more rapid the growth, the lower the percentage

E. 50 % or more

A. Failing to include the right variables

B. Using the wrong forecasting method

C. Employing less sophisticated analysts than necessary

D. Using incorrect data

E. Using standard deviation rather than MAD

A. Weighted moving average

B. Regression

C. Moving average

D. Forecast as a percent of actual

E. Mean absolute deviation

A. 0.2

B. 0.8

C. 1.0

D. 10.0

E. 100.0

A. The forecasting model is operating acceptably

B. The forecasting model is out of control and needs to be corrected

C. The MAD value is incorrect

D. The upper control value is less than 20

E. It is using an inappropriate forecasting methodology

A. The forecasting model is operating acceptably

B. The forecasting model is out of control and needs to be corrected

C. The MAD value is incorrect

D. The upper control value is less than 20

E. The company is using an inappropriate forecasting methodology

A. A trend

B. A causal relationship

C. A statistical correlation

D. A coincidence

E. A fad

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