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Q:
Heavy sales of umbrellas during a rain storm is an example of which of the following?
A. A trend
B. A causal relationship
C. A statistical correlation
D. A coincidence
E. A fad
Q:
Decomposition of time series means identifying and separating time series data into potentially which of the following?
A. Standard error of estimate
B. Alpha components
C. Seasonal components
D. Tracking components
E. Intercept components
Q:
A company hires you to develop a linear regression forecasting model. Based on the company's historical sales information, you determine the intercept value of the model to be 1,200. You also find the slope value is -50. If after developing the model you are given a value of X = 10, which of the following is the resulting forecast value using this model?
A. 3,800
B. 700
C. 1,700
D. 1,040
E. 12,000
Q:
If the intercept value of a linear regression model is 40, the slope value is 40 and the value of X is 40, which of the following is the resulting forecast value using this model?
A. 120
B. 1,600
C. 1,640
D. 2,200
E. 64,000
Q:
Which of the following is the portion of observations you would expect to see lying within a plus or minus 2 MAD range?
A. 57.048
B. 88.946
C. 98.334
D. 99.856
E. 100
Q:
Which of the following is the portion of observations you would expect to see lying within a plus or minus 3 MAD range?
A. 57.048 percent
B. 88.946 percent
C. 98.334 percent
D. 99.856 percent
E. 100 percent
Q:
You are hired as a consultant to advise a small firm on forecasting methodology. Based on your research you find the company has a MAD of 3. Its wants to have a 99.7 percent control limits on its forecasting system. It's most recent tracking signal value is 15. What should be your report to the company?
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
Q:
A company has a MAD of 10. Its wants to have a 99.7 percent control limits on its forecasting system. It's most recent tracking signal value is 31. What can the company conclude from this information?
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
Q:
A company has calculated its running sum of forecast errors to be 500 and its mean absolute deviation is exactly 35. Which of the following is the company's tracking signal?
A. Cannot be calculated based on this information
B. About 14.3
C. More than 35
D. Exactly 35
E. About 0.07
Q:
If you were selecting a forecasting model based on MAD, which of the following MAD values reflects the most accurate model?
A. 0.2
B. 0.8
C. 1.0
D. 10.0
E. 100.0
Q:
A company has actual unit demand for four consecutive years of 100, 105, 135 and 150. The respective forecasts were 120 for all four years. Which of the following is the resulting MAD value that can be computed from this data?
A. 2.5
B. 10
C. 20
D. 22.5
E. 30
Q:
A company has actual unit demand for three consecutive years of 124, 126 and 135. The respective forecasts for the same three years are 120, 120 and 130. Which of the following is the resulting MAD value that can be computed from this data?
A. 1
B. 3
C. 5
D. 15
E. 123
Q:
Which of the following are used to describe the degree of error?
A. Weighted moving average
B. Regression
C. Moving average
D. Focus forecast
E. Mean absolute deviation
Q:
Which of the following is a possible source of bias error in forecasting?
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
Q:
As a consultant you have been asked to generate a unit demand forecast for a product for year 2009 using exponential smoothing. The actual demand in year 2008 was 750. The forecast demand in year 2008 was 960. Using this data and a smoothing constant alpha of 0.3, which of the following is the resulting year 2009 forecast value?
A. 766
B. 813
C. 897
D. 1,023
E. 1,120
Q:
A company wants to generate a forecast for unit demand for year 2009 using exponential smoothing. The actual demand in year 2008 was 120. The forecast demand in year 2008 was 110. Using this data and a smoothing constant alpha of 0.1, which of the following is the resulting year 2009 forecast value?
A. 100
B. 110
C. 111
D. 114
E. 120
Q:
Given a prior forecast demand value of 1,100, a related actual demand value of 1,000 and a smoothing constant alpha of 0.3, what is the exponential smoothing forecast value?
A. 1,000
B. 1,030
C. 1,070
D. 1,130
E. 970
Q:
If a firm produced a product that is experiencing growth in demand, the smoothing constant alpha used in an exponential smoothing forecasting model would tend to be which of the following?
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
Q:
If a firm produced a standard item with relatively stable demand, the smoothing constant alpha used in an exponential smoothing forecasting model would tend to be in which of the following ranges?
A. 5% to 10%
B. 20% to 50%
C. 20% to 80%
D. 60% to 120%
E. 90% to 100%
Q:
Given a prior forecast demand value of 230, a related actual demand value of 250 and a smoothing constant alpha of 0.1, what is the exponential smoothing forecast value for the following period?
A. 230
B. 232
C. 238
D. 248
E. 250
Q:
The exponential smoothing method requires which of the following data to forecast the future?
A. The most recent forecast, the most recent demand and the smoothing constant
B. Precise actual demand for the past 10-15 years
C. The slope of the trendline and the seasonality peak-to-trough ratio
D. Overall industry demand data
E. Tracking values alpha, beta and gamma
Q:
Which of the following is the major reason that exponential smoothing has become well accepted as a forecasting technique?
A. Accuracy
B. Sophistication of analysis
C. Predicts turning points
D. Ease of use
E. Forecasts lag data trends
Q:
A company wants to forecast demand using the weighted moving average. If the company uses three prior yearly sales values (i.e., year 2006 = 160, year 2007 = 140 and year 2008 = 170) and we want to weight year 2006 at 30%, year 2007 at 30% and year 2008 at 40%, which of the following is the weighted moving average forecast for year 2009?
A. 170
B. 168
C. 158
D. 152
E. 146
Q:
A company wants to forecast demand using the weighted moving average. If the company uses two prior yearly sales values (i.e., year 2007 = 110 and year 2008 = 130) and we want to weight year 2007 at 10% and year 2008 at 90%, which of the following is the weighted moving average forecast for year 2009?
A. 120
B. 128
C. 133
D. 138
E. 142
Q:
A company wants to forecast demand using the simple moving average. If the company uses three prior yearly sales values (i.e., year 2006 = 130, year 2007 = 110 and year 2008 =160), which of the following is the simple moving average forecast for year 2009?
A. 100.5
B. 122.5
C. 133.3
D. 135.6
E. 139.3
Q:
A company wants to forecast demand using the simple moving average. If the company uses four prior yearly sales values (i.e., year 2005 = 100, year 2006 = 120, year 2007 = 140 and year 2008 = 210), which of the following is the simple moving average forecast for year 2009?
A. 100.5
B. 140.0
C. 142.5
D. 145.5
E. 155.0
Q:
Which of the following considerations is not usually a factor in deciding which forecasting model a firm should choose?
A. Time horizon to forecast
B. Product
C. Accuracy required
D. Data availability
E. Analyst sophistication
Q:
Which of the following forecasting methods can be used for short-term forecasting?
A. Simple exponential smoothing
B. Delphi technique
C. Market research
D. Hoskins-Hamilton smoothing
E. Serial regression
Q:
In general, which forecasting time frame is best to detect general trends?
A. Short-term forecasts
B. Quick-time forecasts
C. Long range forecasts
D. Medium term forecasts
E. Rapid change forecasts
Q:
In general, which forecasting time frame best identifies seasonal effects?
A. Short-term forecasts
B. Quick-time forecasts
C. Long range forecasts
D. Medium term forecasts
E. Rapid change forecasts
Q:
In general, which forecasting time frame compensates most effectively for random variation and short term changes?
A. Short-term forecasts
B. Quick-time forecasts
C. Long range forecasts
D. Medium term forecasts
E. Rapid change forecasts
Q:
In business forecasting, what is usually considered a long-term time period?
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
Q:
In business forecasting, what is usually considered a medium-term time period?
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
Q:
In business forecasting, what is usually considered a short-term time period?
A. Four weeks or less
B. More than three months
C. Six months or more
D. Less than three months
E. One year
Q:
Which of the following forecasting methods is very dependent on selection of the right individuals who will judgmentally be used to actually generate the forecast?
A. Time series analysis
B. Simple moving average
C. Weighted moving average
D. Delphi method
E. Panel consensus
Q:
Which of the following forecasting methods uses executive judgment as its primary component for forecasting?
A. Historical analogy
B. Time series analysis
C. Panel consensus
D. Grass roots
E. Focus forecasting
Q:
"Dynamic models, usually computer-based, that allow the forecaster to make assumptions about the internal variables and external environment in the model" is a definition for which of the following forecasting methodologies?
A. Causal
B. Simulation
C. Focus forecasting
D. Shiskin Time series
E. Market research
Q:
Which of the following forecasting methodologies is considered a causal forecasting technique?
A. Market research
B. Historical analogy
C. Delphi Method
D. Input/output models
E. Simulation
Q:
Which of the following forecasting methodologies is considered a causal forecasting technique?
A. Box Jenkins Technique
B. Weighted moving average
C. Leading indicators
D. Historical analogy
E. Focus forecasting
Q:
Which of the following forecasting methodologies is considered a time series forecasting technique?
A. Input/output models
B. Exponential averaging
C. Simple movement smoothing
D. Weighted moving average
E. Simulation
Q:
Which of the following forecasting methodologies is considered a time series forecasting technique?
A. Box Jenkins technique
B. Econometrics
C. Leading indicators
D. Historical analogy
E. Simulation
Q:
Which of the following forecasting methodologies is considered a qualitative forecasting technique?
A. Grass roots
B. Econometric models
C. Regression analysis
D. Exponential smoothing
E. Simple moving average
Q:
Which of the following forecasting methodologies is considered a qualitative forecasting technique?
A. Simple moving average
B. Market research
C. Linear regression
D. Focus forecasting
E. Multiple regression
Q:
In most cases, demand for products or services can be broken into several components. Which of the following is considered a component of demand?
A. Forecast error
B. Autocorrelation
C. Previous demand
D. Consistent demand
E. Repeat demand
Q:
In most cases, demand for products or services can be broken into several components. Which of the following is considered a component of demand?
A. Cyclical elements
B. Future demand
C. Past demand
D. Inconsistent demand
E. Level demand
Q:
In most cases, demand for products or services can be broken into several components. Which of the following is not considered a component of demand?
A. Average demand for a period
B. A trend
C. Seasonal elements
D. Past demand
E. Autocorrelation
Q:
Which of the following is not one of the basic types of forecasting?
A. Qualitative
B. Time series analysis
C. Causal relationships
D. Simulation
E. Force field analysis
Q:
A good forecaster is one who develops special skills and experience at one forecasting technique and is capable of applying it to widely diverse situations.
Q:
For every forecasting problem there is one best forecasting technique.
Q:
Focus forecasting is an approach to forecasting that tries several rules that seem logical and easy to understand to project past data into the future.
Q:
Multiple regression analysis uses several regression models to generate a forecast.
Q:
Additive seasonal variation in time series data means a forecast can only be found by multiplying the trend times the seasonal factor (or index).
Q:
The standard error of the estimate of a linear regression is not useful for judging the fit between the data and the regression line when doing forecasts.
Q:
Linear regression is not useful for aggregate planning.
Q:
Regression is a functional relationship between two or more correlated variables, where one variable is used to predict another.
Q:
A major limitation of linear regression as a model for forecasting is that past data and future projections are assumed to fall on or near a straight line.
Q:
A tracking signal (TS) can be calculated using the arithmetic sum of forecast deviations divided by the MAD.
Q:
RSFE in forecasting stands for "readable safety function for error detection".
Q:
MAD statistics can be used to generate tracking signals.
Q:
When forecast errors occur in a normally distributed pattern, the ratio of the mean absolute deviation to the standard deviation is 2 to 1 or 2 x MAD = 1 standard deviation.
Q:
Random errors in forecasting occur when an undetected secular trend is not included in a forecasting model.
Q:
Random errors can be defined as those that cannot be explained by the forecast model being used.
Q:
Because the factors governing demand for products are very complex, all forecasts of demand contain some error.
Q:
Exponential smoothing forecasts always lag behind the actual occurrence but can be corrected somewhat with a trend adjustment.
Q:
Simple exponential smoothing lags changes in demand.
Q:
The value of the smoothing constant alpha in an exponential smoothing model is between 0 and 1.
Q:
The simple moving average model requires linear forecast values.
Q:
The simple moving average model permits non-linear forecast values.
Q:
The weighted moving average model does not work with non-linear forecast values.
Q:
The exponential smoothing model permits non-linear forecast values.
Q:
In exponential smoothing, it is desirable to use a higher smoothing constant when forecasting demand for a product experiencing high growth.
Q:
Focus forecasting is the most accurate of all forecasting models.
Q:
Exponential smoothing is always the most accurate of all forecasting models.
Q:
The equation for exponential smoothing states that the new forecast is equal to the old forecast plus the error of the old forecast.
Q:
A central premise of exponential smoothing is that more recent data is less indicative of the future than data from the distant past.
Q:
The weighted moving average forecasting model uses a weighting scheme to modify the effects of individual data points. This is its major advantage over the simple moving average model.
Q:
Experience and trial and error are the simplest ways to choose weights for the weighted moving average forecasting model.
Q:
Box-Jenkins forecasting models deliver very precise forecasts and are most useful to people with unsophisticated analytical backgrounds.
Q:
People with no or modest analytical backgrounds can make good use of causal regression forecasting models.
Q:
In the simple exponential smoothing forecasting model you need at least 100 observations to set the weight.