ch4 Flashcards
1
Q
T|F: A naïve forecast for September sales of a product would be equal to the forecast for August.
A
F
2
Q
T|F: The forecasting time horizon and the forecasting techniques used tend to vary over the life cycle of a product.
A
T
3
Q
T|F: Demand (sales) forecasts serve as inputs to financial, marketing, and personnel planning.
A
T
4
Q
T|F: Forecasts of individual products tend to be more accurate than forecasts of product families.
A
F
5
Q
T|F: Most forecasting techniques assume that there is some underlying stability in the system.
A
T
6
Q
T|F: The sales force composite forecasting method relies on salespersons’ estimates of expected sales.
A
T
7
Q
T|F: A time-series model uses a series of past data points to make the forecast.
A
T
8
Q
T|F: The quarterly “make meeting” of Lexus dealers is an example of a sales force composite forecast.
A
T
9
Q
T|F: Cycles and random variations are both components of time series.
A
T
10
Q
T|F: A naive forecast for September sales of a product would be equal to the sales in August.
A
T
11
Q
T|F: One advantage of exponential smoothing is the limited amount of record keeping involved.
A
T
12
Q
T|F: The larger the number of periods in the simple moving average forecasting method, the greater the
method’s responsiveness to changes in demand.
A
F
13
Q
T|F: Forecast including trend is an exponential smoothing technique that utilizes two smoothing
constants: one for the average level of the forecast and one for its trend.
A
T
14
Q
T|F: Mean Squared Error and Coefficient of Correlation are two measures of the overall error of a
forecasting model.
A
F
15
Q
T|F: In trend projection, the trend component is the slope of the regression equation.
A
T
16
Q
T|F: In trend projection, a negative regression slope is mathematically impossible.
A
F
17
Q
T|F: Seasonal indexes adjust raw data for patterns that repeat at regular time intervals.
A
T
18
Q
T|F: If a quarterly seasonal index has been calculated at 1.55 for the October-December quarter, then
raw data for that quarter must be multiplied by 1.55 so that the quarter can be fairly compared to
other quarters.
A
F
19
Q
T|F: The best way to forecast a business cycle is by finding a leading variable.
A
T
20
Q
T|F: Linear-regression analysis is a straight-line mathematical model to describe the functional
relationships between independent and dependent variables.
A
T
21
Q
T|F: The larger the standard error of the estimate, the more accurate the forecasting model.
A
F
22
Q
T|F: A trend projection equation with a slope of 0.78 means that there is a 0.78 unit rise in Y for every
unit of time that passes.
A
T
23
Q
T|F: In a regression equation where Y is demand and X is advertising, a coefficient of determination
(R2
) of .70 means that 70% of the variance in advertising is explained by demand.
A
F
24
Q
T|F: Demand cycles for individual products can be driven by product life cycles.
A
T
25
T|F: If a forecast is consistently greater than (or less than) actual values, the forecast is said to be biased.
T
26
T|F: Focus forecasting tries a variety of computer models and selects the best one for a particular
application.
T
27
T|F: Many service firms use point-of-sale computers to collect detailed records needed for accurate
short-term forecasts.
T
28
What two numbers are contained in the daily report to the CEO of Walt Disney Parks & Resorts
regarding the six Orlando parks?
a. yesterday’s forecasted attendance and yesterday’s actual attendance
b. yesterday’s actual attendance and today’s forecasted attendance
c. yesterday’s forecasted attendance and today’s forecasted attendance
d. yesterday’s actual attendance and last year’s actual attendance
e. yesterday’s forecasted attendance and the year-to-date average daily forecast error
a. yesterday’s forecasted attendance and yesterday’s actual attendance
29
Using an exponential smoothing model with smoothing constant α = .20, how much weight would
be assigned to the 2nd most recent period?
a. .16
b. .20
c. .04
d. .09
e. .10
a. .16
30
Forecasts
a. become more accurate with longer time horizons
b. are rarely perfect
c. are more accurate for individual items than for groups of items
d. all of the above
e. none of the above
b. are rarely perfect
31
One use of short-range forecasts is to determine
a. production planning
b. inventory budgets
c. research and development plans
d. facility location
e. job assignments
e. job assignments
32
Forecasts are usually classified by time horizon into three categories
a. short-range, medium-range, and long-range
b. finance/accounting, marketing, and operations
c. strategic, tactical, and operational
d. exponential smoothing, regression, and time series
e. departmental, organizational, and industrial
a. short-range, medium-range, and long-range
33
A forecast with a time horizon of about 3 months to 3 years is typically called a
a. long-range forecast
b. medium-range forecast
c. short-range forecast
d. weather forecast
e. strategic forecast
b. medium-range forecast
34
Forecasts used for new product planning, capital expenditures, facility location or expansion, and
R&D typically utilize a
a. short-range time horizon
b. medium-range time horizon
c. long-range time horizon
d. naive method, because there is no data history
e. all of the above
c. long-range time horizon
35
The three major types of forecasts used by business organizations are
a. strategic, tactical, and operational
b. economic, technological, and demand
c. exponential smoothing, Delphi, and regression
d. causal, time-series, and seasonal
e. departmental, organizational, and territorial
b. economic, technological, and demand
36
Which of the following is not a step in the forecasting process?
a. Determine the use of the forecast.
b. Eliminate any assumptions.
c. Determine the time horizon.
d. Select forecasting model.
e. Validate and implement the results.
b. Eliminate any assumptions.
37
The two general approaches to forecasting are
a. qualitative and quantitative
b. mathematical and statistical
c. judgmental and qualitative
d. historical and associative
e. judgmental and associative
a. qualitative and quantitative
38
Which of the following uses three types of participants: decision makers, staff personnel, and
respondents?
a. executive opinions
b. sales force composites
c. the Delphi method
d. consumer surveys
e. time series analysis
c. the Delphi method
39
The forecasting model that pools the opinions of a group of experts or managers is known as the
a. sales force composition model
b. multiple regression
c. jury of executive opinion model
d. consumer market survey model
e. management coefficients model
c. jury of executive opinion model
40
Which of the following is not a type of qualitative forecasting?
a. executive opinions
b. sales force composites
c. consumer surveys
d. the Delphi method
e. moving average
e. moving average
41
Which of the following techniques uses variables such as price and promotional expenditures,
which are related to product demand, to predict demand?
a. associative models
b. exponential smoothing
c. weighted moving average
d. simple moving average
e. time series
a. associative models
42
Which of the following statements about time series forecasting is true?
a. It is based on the assumption that future demand will be the same as past demand.
b. It makes extensive use of the data collected in the qualitative approach.
c. The analysis of past demand helps predict future demand.
d. Because it accounts for trends, cycles, and seasonal patterns, it is more powerful than causal
forecasting.
e. All of the above are true.
c. The analysis of past demand helps predict future demand
43
Time series data may exhibit which of the following behaviors?
a. trend
b. random variations
c. seasonality
d. cycles
e. They may exhibit all of the above.
e. They may exhibit all of the above.
44
Gradual, long-term movement in time series data is called
a. seasonal variation
b. cycles
c. trends
d. exponential variation
e. random variation
c. trends
45
Which of the following is not present in a time series?
a. seasonality
b. operational variations
c. trend
d. cycles
e. random variations
b. operational variations
46
The fundamental difference between cycles and seasonality is the
a. duration of the repeating patterns
b. magnitude of the variation
c. ability to attribute the pattern to a cause
d. all of the above
e. none of the above
a. duration of the repeating patterns
47
In time series, which of the following cannot be predicted?
a. large increases in demand
b. technological trends
c. seasonal fluctuations
d. random fluctuations
e. large decreases in demand
d. random fluctuations
48
What is the approximate forecast for May using a four-month moving average?
Nov. Dec. Jan. Feb. Mar. April
39 36 40 42 48 46
44
49
Which time series model below assumes that demand in the next period will be equal to the most
recent period's demand?
a. naive approach
b. moving average approach
c. weighted moving average approach
d. exponential smoothing approach
e. none of the above
a. naive approach
50
John’s House of Pancakes uses a weighted moving average method to forecast pancake sales. It
assigns a weight of 5 to the previous month’s demand, 3 to demand two months ago, and 1 to
demand three months ago. If sales amounted to 1000 pancakes in May, 2200 pancakes in June, and
3000 pancakes in July, what should be the forecast for August?
a. 2400
b. 2511
c. 2067
d. 3767
e. 1622
b. 2511
51
A six-month moving average forecast is better than a three-month moving average forecast if
demand
a. is rather stable
b. has been changing due to recent promotional efforts
c. follows a downward trend
d. follows a seasonal pattern that repeats itself twice a year
e. follows an upward trend
a. is rather stable
52
Increasing the number of periods in a moving average will accomplish greater smoothing, but at
the expense of
a. manager understanding
b. accuracy
c. stability
d. responsiveness to changes
e. All of the above are diminished when the number of periods increases.
d. responsiveness to changes
53
Which of the following statements comparing the weighted moving average technique and
exponential smoothing is true?
a. Exponential smoothing is more easily used in combination with the Delphi method.
b. More emphasis can be placed on recent values using the weighted moving average.
c. Exponential smoothing is considerably more difficult to implement on a computer.
d. Exponential smoothing typically requires less record keeping of past data.
e. Exponential smoothing allows one to develop forecasts for multiple periods, whereas weighted
moving averages does not.
d. Exponential smoothing typically requires less record keeping of past data.
54
Which time series model uses past forecasts and past demand data to generate a new forecast?
a. naive
b. moving average
c. weighted moving average
d. exponential smoothing
e. regression analysis
d. exponential smoothing
55
Which is not a characteristic of exponential smoothing?
a. smoothes random variations in the data
b. easily altered weighting scheme
c. weights each historical value equally
d. has minimal data storage requirements
e. none of the above; they are all characteristics of exponential smoothing
c. weights each historical value equally
56
Which of the following smoothing constants would make an exponential smoothing forecast
equivalent to a naive forecast?
a. 0
b. 1 divided by the number of periods
c. 0.5
d. 1.0
e. cannot be determined
d. 1.0
57
Given an actual demand of 103, a previous forecast value of 99, and an alpha of .4, the exponential
smoothing forecast for the next period would be
a. 94.6
b. 97.4
c. 100.6
d. 101.6
e. 103.0
c. 100.6
58
A forecast based on the previous forecast plus a percentage of the forecast error is a(n)
a. qualitative forecast
b. naive forecast
c. moving average forecast
d. weighted moving average forecast
e. exponentially smoothed forecast
e. exponentially smoothed forecast
59
Given an actual demand of 61, a previous forecast of 58, and an α of .3, what would the forecast
for the next period be using simple exponential smoothing?
a. 45.5
b. 57.1
c. 58.9
d. 61.0
e. 65.5
c. 58.9
60
Which of the following values of alpha would cause exponential smoothing to respond the most
slowly to forecast errors?
a. 0.10
b. 0.20
c. 0.40
d. 0.80
e. cannot be determined
a. 0.10
61
The primary purpose of the mean absolute deviation (MAD) in forecasting is to
a. estimate the trend line
b. eliminate forecast errors
c. measure forecast accuracy
d. seasonally adjust the forecast
e. all of the above
c. measure forecast accuracy
62
Given forecast errors of -1, 4, 8, and -3, what is the mean absolute deviation?
a. 2
b. 3
c. 4
d. 8
e. 16
c. 4
63
The last four months of sales were 8, 10, 15, and 9 units. The last four forecasts were 5, 6, 11, and
12 units. The Mean Absolute Deviation (MAD) is
a. 2
b. -10
c. 3.5
d. 9
e. 10.5
c. 3.5
64
A time series trend equation is 25.3 + 2.1 X. What is your forecast for period 7?
a. 23.2
b. 25.3
c. 27.4
d. 40.0
e. cannot be determined
d. 40.0
65
For a given product demand, the time series trend equation is 53 - 4 X. The negative sign on the
slope of the equation
a. is a mathematical impossibility
b. is an indication that the forecast is biased, with forecast values lower than actual values
c. is an indication that product demand is declining
d. implies that the coefficient of determination will also be negative
e. implies that the RSFE will be negative
c. is an indication that product demand is declining
66
Yamaha manufacturers which set of products with complementary demands to address seasonal
fluctuations?
a. golf clubs and skis
b. swimming suits and winter jackets
c. jet skis and snowmobiles
d. pianos and guitars
e. ice skates and water skis
c. jet skis and snowmobiles
67
Which of the following is true regarding the two smoothing constants of the Forecast Including
Trend (FIT) model?
a. One constant is positive, while the other is negative.
b. They are called MAD and RSFE.
c. Alpha is always smaller than beta.
d. One constant smoothes the regression intercept, whereas the other smoothes the regression
slope.
e. Their values are determined independently.
e. Their values are determined independently.
68
Demand for a certain product is forecast to be 800 units per month, averaged over all 12 months of
the year. The product follows a seasonal pattern, for which the January monthly index is 1.25.
What is the seasonally-adjusted sales forecast for January?
a. 640 units
b. 798.75 units
c. 800 units
d. 1000 units
e. cannot be calculated with the information given
d. 1000 units
69
A seasonal index for a monthly series is about to be calculated on the basis of three years'
accumulation of data. The three previous July values were 110, 150, and 130. The average over all
months is 190. The approximate seasonal index for July is
a. 0.487
b. 0.684
c. 1.462
d. 2.053
e. cannot be calculated with the information given
b. 0.684
70
A fundamental distinction between trend projection and linear regression is that
a. trend projection uses least squares while linear regression does not
b. only linear regression can have a negative slope
c. in trend projection the independent variable is time; in linear regression the independent
variable need not be time, but can be any variable with explanatory power
d. linear regression tends to work better on data that lack trends
e. trend projection uses two smoothing constants, not just one
c. in trend projection the independent variable is time; in linear regression the independent
variable need not be time, but can be any variable with explanatory power
71
The percent of variation in the dependent variable that is explained by the regression equation is
measured by the
a. mean absolute deviation
b. slope
c. coefficient of determination
d. correlation coefficient
e. intercept
c. coefficient of determination
72
The degree or strength of a linear relationship is shown by the
a. alpha
b. mean
c. mean absolute deviation
d. correlation coefficient
e. RSFE
d. correlation coefficient
73
If two variables were perfectly correlated, the correlation coefficient r would equal
a. 0
b. -1
c. 1
d. b or c
e. none of the above
d. b or c
74
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
a. qualitative methods
b. adaptive smoothing
c. slope
d. bias
e. trend projection
d. bias
75
The tracking signal is the
a. standard error of the estimate
b. running sum of forecast errors (RSFE)
c. mean absolute deviation (MAD)
d. ratio RSFE/MAD
e. mean absolute percentage error (MAPE)
d. ratio RSFE/MAD
76
Computer monitoring of tracking signals and self-adjustment if a signal passes a preset limit is
characteristic of
a. exponential smoothing including trend
b. adaptive smoothing
c. trend projection
d. focus forecasting
e. multiple regression analysis
b. adaptive smoothing
77
Many services maintain records of sales noting
a. the day of the week
b. unusual events
c. weather
d. holidays
e. all of the above
e. all of the above
78
Taco Bell's unique employee scheduling practices are partly the result of using
a. point-of-sale computers to track food sales in 15 minute intervals
b. focus forecasting
c. a six-week moving average forecasting technique
d. multiple regression
e. a and c are both correct
e. a and c are both correct
79
------forecasts are concerned with rates of technological progress, which can result in the
birth of exciting new products, requiring new plants and equipment.
Technological
80
_________ forecasts address the business cycle by predicting inflation rates, money supplies,
housing starts, and other planning indicators.
Economic
81
Demand forecasts, also called _________ forecasts, are projections of demand for a company's
products or services.
sales
82
__________ forecasts employ one or more mathematical models that rely on historical data and/or
causal variables to forecast demand.
Quantitative
83
___________ is a forecasting technique based upon salespersons' estimates of expected sales.
Sales force composite
84
__________ forecasts use a series of past data points to make a forecast.
Time-series
85
A(n) ______________ forecast uses an average of the most recent periods of data to forecast the
next period.
moving average
86
The smoothing constant is a weighting factor used in ______________.
exponential smoothing
87
Linear regression is known as a(n) _____________ because it incorporates variables or factors that
might influence the quantity being forecast.
associative model
88
A measure of forecast error that does not depend on the magnitude of the item being forecast is the
___________.
mean absolute percent error or MAPE
89
_____________ is a measure of overall forecast error for a model.
MAD or Mean Absolute Deviation
90
When one constant is used to smooth the forecast average and a second constant is used to smooth
the trend, the forecasting method is __________________.
exponential smoothing with trend adjustment or trend-adjusted smoothing or second-order
smoothing or double smoothing
91
____________ is a time-series forecasting method that fits a trend line to a series of historical data
points and then projects the line into the future for forecasts.
Trend projection
92
The ______________________ measures the strength of the relationship between two variables.
coefficient of correlation
93
If a barbershop operator noted that Tuesday's business was typically twice as heavy as
Wednesday's, and that Friday's business was typically the busiest of the week, business at the
barbershop is subject to ____________.
seasonal variations
94
__________ forecasting tries a variety of computer models and selects the best one for a particular
application.
Focus
