Forecasting and Predictive Analytics with ForecastX Barry Keating 7th Edition-Test Bank
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Sample Test
Forecasting and Predictive Analytics with Forecast X, 7e
(Keating)
Chapter 3 Extrapolation 1. Moving Averages and Exponential
Smoothing
1) What factors do the data smoothing techniques presented in
Chapter Three have in common?
1. A)
They all use only past observations of the data.
2. B)
They all fail to forecast cyclical reversals in the data.
3. C)
They all smooth short-term noise by averaging data.
4. D)
They all produce serially correlated forecasts.
5. E)
All of the options are correct.
2) Time series smoothing techniques work best for applications
where
1. A)
little historical data are available to the forecaster.
2. B) there
is a large amount of historical data available.
3. C)
the forecast horizon is the distant future.
4. D)
only periodic forecasts for untimely events are required.
5. E)
All of the options are correct.
3) Time-series smoothing techniques attempt to
1. A)
suppress short-term variability in the data.
2. B)
identify long-term trends or cycles in the data.
3. C)
remove seasonality in the data.
4. D)
suppress data noise while extracting trend.
5. E)
All of the options are correct.
4) A simple-centered 3-point moving average of the time-series
variable Xt is given by:
3. A)
(Xt-1 + Xt-2 + Xt-3)/3.
4. B)
(Xt + Xt-1 + Xt-2)/3.
5. C)
(Xt+1 + Xt + Xt-1)/3.
6. D)
None of the options are correct.
5) Which of the following is not a problem with moving-average
forecasting?
1. A) It
produces serially correlated forecasts.
2. B) It
removes short-term variability by averaging nearby data.
3. C) It
cannot predict reversals in trends.
4. D) It
cannot model non-stationary data.
5. E)
All of the options are correct.
6) With which type of time-series data should moving-average
smoothing methods produce the best forecasts?
1. A)
Seasonal.
2. B)
Stationary.
3. C)
Trending.
4. D)
Cyclical.
5. E)
All of the options are correct.
7) In using moving-average smoothing to generate forecasts, a
three-month moving average will be preferred to a six-month moving average
1. A) if
the true data cycle is three months.
2. B) if
it has a lower RMSE.
3. C) if
it has a lower mean-squared error.
4. D) if
we have very little data to work with.
5. E)
All of the options are correct.
8) Moving-average smoothing may lead to misleading inference
when applied to
1. A)
stationary data.
2. B)
forecasting trend reversal in the stock market.
3. C)
small and limited data sets.
4. D)
large and plentiful data sets.
5. E)
None of the options are correct.
9) Which method uses an arithmetic mean to forecast the next
period?
1. A)
Naïve.
2. B)
Moving averages.
3. C)
Exponential smoothing.
4. D)
Adaptive filtering.
5. E)
None of the options are correct.
10) Some drawbacks to using centered moving-average smoothing
models include
1. A)
loss of data at each end of the original time series.
2. B)
introduction of autocorrelation into the forecasts.
3. C)
inability to forecast turning points in the data.
4. D)
All of the options are correct.
11) Which forecasting model assumes that the pattern exhibited
by historical data can best be represented by an arithmetic average of nearby
observations?
1. A)
Simple exponential smoothing.
2. B)
Naïve methods.
3. C)
Moving average smoothing.
4. D)
Holt’s smoothing.
5. E)
None of the options are correct.
12) Which method is used to develop a simple model that assumes
that weighted averages of past periods are the best predictors of the future?
1. A)
Naïve.
2. B)
Moving averages.
3. C)
Exponential smoothing.
4. D)
Naïve model squared.
5. E)
None of the options are correct.
13) Simple-exponential smoothing models are useful for data
which have
1. A) a
downward time trend.
2. B) an
upward time trend.
3. C)
neither an upward or downward time trend.
4. D)
pronounced seasonality.
5. E)
All of the options are correct.
14) Simple-exponential smoothing models differ from moving
average models in that
1. A)
moving average models use weighted averages of the data whereas simple
exponential smoothing models use simple averages.
2. B)
simple exponential smoothing models use weighted averages of the data whereas
moving average models use simple averages.
15) Which of the following is a factor in the decision to use
exponential smoothing rather than moving-average smoothing to forecast a given
time series?
1. A)
Amount of data available.
2. B)
Importance of recent past versus distant past.
3. C)
Forecast horizon.
4. D)
Expertise of the forecast manager.
5. E)
None of the options are correct.
16) The term ‘exponential’ in the exponential smoothing method
refers to
1. A)
weights on past data that increase exponentially into the past.
2. B) weights
on past data that decrease exponentially into the past.
3. C)
calculation uses a weighted average.
4. D)
using a non-weighted polynomial on past data.
5. E)
None of the options are correct.
17) The error-correction form of the simple exponential
smoothing model states that if the current forecast
1. A)
error was zero, the current forecast could be used to forecast next period’s
level.
2. B)
overstated the actual level, the forecast of the level next period will be
revised downward.
3. C)
understated the actual level, the forecast of the level next period will be
revised upward.
4. D)
All of the options are correct.
18) Which of the following is not correct concerning choosing
the appropriate size of the level smoothing constant (α or alpha) in the simple
exponential smoothing model?
1. A)
Select values close to zero if the series has a great deal of random variation.
2. B)
Select values close to one if you wish the forecast values to depend strongly
on recent changes in the actual values.
3. C)
Select a value that minimizes RMSE.
4. D)
Select a value that maximizes mean-squared error.
5. E)
All of the options are correct.
19) The simple exponential smoothing model can be expressed as
1. A) a
simple average of past values of the data.
2. B) an
expression combining the most recent forecast and actual data value.
3. C) a
weighted average, where the weights sum to zero.
4. D) a
weighted average, where the weights sum to the sample size.
5. E)
None of the options are correct.
20) The same benefits/criticisms apply to moving average and
exponential smoothing with the exception of
1. A)
amount of data required.
2. B)
ease of calculation.
3. C)
ability to model trend.
4. D)
ability to forecast cyclical reversals.
5. E)
None of the options are correct.
21) Choosing the appropriate size of the level smoothing
constant (α) in the simple exponential smoothing model
1. A) is
equivalent to asking, “How much weight should be given in revising our forecast
for next period to this period’s forecast error?”
2. B)
can best be determined by subjective means.
3. C) is
simple if the data are stationary since α should be zero.
4. D) is
simple if the data are nonstationary since α should be one.
22) The smoothing constant in the exponential smoothing model
1. A)
completely determines the weight structure in exponential smoothing.
2. B)
can be interpreted as the revision of this period’s forecast to today’s
forecast error.
3. C)
cannot be equal to 0 or 1.
4. D)
must lie between 0 and 1.
5. E)
All of the options are correct.
23) Which of the following is not a major problem with
exponential smoothing?
1. A) It
requires a large amount of data and time to generate forecasts.
2. B) It
requires that the forecaster choose, on some basis, the smoothing constant.
3. C) It
produces forecasts that are serially correlated.
4. D) It
employs only past data in making forecasts of the future.
5. E)
All of the options are correct.
24) Which of the following is not considered a smoothing model?
1. A)
Naïve.
2. B)
Moving averages.
3. C)
Exponential smoothing.
4. D)
Adaptive-Response-Rate Single Exponential Smoothing.
5. E)
None of the options are correct.
25) Simple Smoothing
Time Period Actual
Series Forecast Series Forecast Error
1
100
100
0
2
110
3
115
If a smoothing constant of .3 is used, what is the exponentially
smoothed forecast for period 4?
106.
A) 106.6.
107.
B) 103.0.
108.
C) 115.0.
109.
D) 112.6.
110.
E) 104.4.
26) Simple Smoothing
Time Period Actual Series
Forecast Series Forecast Error
1
100
100
0
2
110
3
115
If a smoothing constant of .3 is used, what is the exponentially
smoothed forecast for period 4?
3. A)
−3.
4. B)
−12.
5. C)
−10.
6. D)
−7.
7. E)
+7.
27) Simple Smoothing
Time Period Actual
Series Forecast Series Forecast Error
1
100
100
0
2
110
3
115
If a three-month moving-average model is used, what is the
forecast for period 4?
104.
A) 104.4.
105.
B) 106.6.
106.
C) 107.1.
107.
D) 108.3.
108.
E) 110.2.
28) If the smoothing constant were chosen to be unity, the
exponential smoothing model would equal
1. A)
moving average smoothing.
2. B)
Holt’s exponential smoothing.
3. C)
the simple naïve model.
4. D)
Winter’s exponential smoothing.
5. E)
moving average smoothing with a one-year lag.
29) What do moving-average smoothing and exponential smoothing
have in common?
1. A)
They both require only a limited amount of data.
2. B)
They both are simple to use.
3. C)
They both are simple to understand.
4. D) They
both have no ability to adjust for trend in the data.
5. E)
All of the options are correct.
30) The level smoothing constant (α) of the simple exponential
smoothing model
1. A)
should have a value close to one if the underlying data is relatively erratic.
2. B)
should have a value close to zero if the underlying data is relatively smooth.
3. C)
should have a value closer to zero, the greater the revision in the current
forecast given the current forecast error.
4. D)
should have a value closer to one, the greater the revision in the current
forecast given the current forecast error.
31) In the Holt’s two-parameter smoothing model, the trend
smoothing parameter Gamma
1. A)
should be close to one when the data has a relatively smooth trend.
2. B)
should be close to zero when the data has a relatively smooth trend.
3. C)
should be close to one when α is close to one.
4. D)
should be close to one when α is one.
32) Holt’s forecasted values
1. A)
contain no estimate of trend in the underlying series.
2. B)
are superior when the underlying data has pronounced seasonality.
3. C)
for periods into the future lie along a straight line.
4. D)
are simple centered moving averages.
5. E)
None of the options are correct.
33) The error-correction representation of Holt’s algorithm
shows
1. A)
how both the level and slope forecasts are revised for current forecast errors.
2. B)
that no adjustment is made to this period’s forecasts when the current forecast
error is zero.
3. C)
how seasonality estimates are revised for current forecast errors.
4. D)
All of the options are correct.
34) The Holt’s forecasting model uses
1. A)
naïve methods.
2. B)
moving averages.
3. C)
exponential smoothing.
4. D)
adaptive filtering.
5. E)
None of the options are correct.
35) Holt’s smoothing is best applied to data that are
1. A) nonseasonal.
2. B)
nonstationary.
3. C)
deseasonalized with a trend.
4. D)
nonstationary and nonseasonal.
5. E)
All of the options are correct.
36) Holt’s model accounts for any growth factor present in a
time series by
1. A)
use of a linear trend.
2. B)
smoothing the most recent trend by last period’s smoothed trend.
3. C)
adding trend estimates to level forecasts.
4. D)
using simple exponential smoothing to estimate a trend factor that is then
combined in a linear fashion with the level forecast.
5. E)
All of the options are correct.
37) Winter’s exponential smoothing
1. A) is
appropriate for data with both trend and seasonal components.
2. B)
models account for seasonality in a multiplicative manner.
3. C)
models have three smoothing parameters.
4. D)
models use only past observations of a time series.
5. E)
All of the options are correct.
38) Which of the following is not an aspect of the Winter’s
exponential smoothing model?
1. A)
Holt’s model extended to deseasonalized data
2. B)
Simple exponential smoothing applied to nonstationary data
3. C)
Seasonality estimates that are themselves smoothed
4. D)
Trend estimates that are themselves smoothed
5. E)
All of the options are correct.
39) As an example of how Winter’s smoothing model deals with
seasonality, how would actual quarter-four sales of a retail firm be
deseasonalized?
1. A) It
would be divided by a seasonal factor.
2. B) It
would be multiplied by a seasonal factor.
3. C) It
would be added with a seasonal factor.
4. D) It
would be subtracted from a seasonal factor.
5. E)
None of the options are correct.
40) Which of the following is not correct? Winter’s exponential
smoothing model adjusts for data seasonality by
1. A)
deseasonalizing the data in an additive fashion.
2. B)
deseasonalizing the data in a multiplicative fashion.
3. C)
use of a smoothing constant applied to seasonality estimates.
4. D)
linear smoothing of seasonality estimates.
5. E)
All of the options are correct.
41) If the time series of interest is highly random, the
seasonal smoothing constant (Beta) of the Winter’s model should be set
1. A)
equal to zero.
2. B) at
a small positive value.
3. C) at
a large positive value but less than unity.
4. D) at
unity.
5. E)
None of the options are correct.
42) How many parameters must the forecaster (or the software)
set using Winter’s exponential smoothing?
1. A) 0.
2. B) 1.
3. C) 2.
4. D) 3.
5. E)
None of the options are correct.
43) How many parameters must the forecaster (or the software)
set using Adaptive-Response-Rate Single Exponential Smoothing?
1. A) 0.
2. B) 1.
3. C) 2.
4. D) 3.
5. E)
None of the options are correct.
44) In the Adaptive-Response-Rate Single Exponential Smoothing
model, the smoothing parameter
1. A) is
not a constant.
2. B)
varies from period to period.
3. C) is
determined by the ratio of the absolute value of the smoothed error divided by
the absolute smoothed error.
4. D) is
the ratio of two smoothed error measures.
5. E)
All of the options are correct.
45) The Adaptive-Response-Rate Single Exponential Smoothing
model is best applied to time series data that are
1. A)
nonstationary.
2. B)
seasonal.
3. C)
seasonal and nonstationary.
4. D)
stationary and seasonal.
5. E)
stationary and nonseasonal.
46) The Adaptive-Response-Rate Single Exponential Smoothing
model is termed adaptive because
1. A) it
responds to changes in the pattern of data.
2. B)
the smoothing parameter changes each period.
3. C) it
has the ability to model changes in the mean of time series.
4. D) it
can virtually take care of itself in generating forecasts.
5. E)
All of the options are correct.
47) The Adaptive-Response-Rate Single Exponential Smoothing
model can be amended to handle seasonal data by
1. A)
first deseasonalizing, then reseasonalizing the data.
2. B)
deseasonalizing the data.
3. C)
reseasonalizing the data.
4. D)
smoothing the data trend first.
5. E)
None of the options are correct.
48)
The simple equation above represents
1. A) a
Logistics function.
2. B) a
Croston intermittent function.
3. C) a
Probit function.
4. D) a
Gompertz function.
49)
The simple equation above represents
1. A) a
Logistics function.
2. B) a
Croston intermittent function.
3. C) a
Probit function.
4. D) a Gompertz
function.
50) Growth models like those used in ForecastX usually model
situations well where a process grows
1. A) at
a more or less constant rate.
2. B)
until reaching saturation.
3. C) in
a linear fashion.
4. D) at
an exponential rate.
51) The growth models used in ForecastX are sometimes called
1. A)
exponential models.
2. B)
smoothing models.
3. C)
event models.
4. D)
diffusion models.
52) The “L” independent variable in the growth models we
examined represents
1. A)
the upper limit of the “Y” variable.
2. B)
the number of observations in the original data set.
3. C)
the growth rate of the dependent variable.
4. D)
the lower limit of the dependent variable.
53) When using a growth model under the assumption that constant
improvement becomes harder to achieve as growth takes place, the best model to
use is
1. A) an
Event model.
2. B) a
Logistics Model.
3. C) a
Gompertz model.
4. D) a
Croston intermittent model.
54) The logistics model
1. A)
looks like an exponential function that is concave upward.
2. B)
looks like an exponential function that is concave downward.
3. C)
looks like an “S” curve.
4. D)
approximates a straight line.
55) When using growth curves such as the Gompertz model or the
Logistics model,
1. A) it
does not matter which model is selected; they are equivalent.
2. B) it
is necessary to have a large data set.
3. C)
only short term forecasts are possible.
4. D) it
is customary to specify a saturation point.
56)
The above equation is used to estimate a Gompertz curve. The “L”
in the equation refers to
1. A)
the growth rate of Y.
2. B)
the growth rate of X.
3. C)
the maximum value of Y.
4. D)
the maximum value of X.
57) The Gompertz growth model
1. A) is
best used when it is harder to achieve constant improvement as a maximum value
is approached.
2. B) is
best used when there are factors that assist in maintaining improvements as the
maximum value is approached.
3. C)
should not be used to estimate new product sales.
4. D) is
always preferred to the Logistics model.
58) “Event Models” as used in ForecastX
1. A)
are a form of exponential smoothing.
2. B)
are a type of growth model.
3. C)
are a type of simple regression.
4. D)
are a type of moving average.
59) “Events” in an Event model could include
1. A)
seasonality, trends, and cyclicality.
2. B)
advertising campaigns, sale prices, and couponing.
3. C)
audit dates and forecasting deadlines.
4. D)
the first sale date, last sale date, and growth rate for an item.
60) Smoothing 2
Accuracy
Measures
Value
Forecast
Statistics
Value
AIC
593.72
Durbin Watson (12) 0.66
BIC
597.61
Mean 351,007.33
MAPE
1.84
%
Standard Deviation 80,306.64
R-Square
97.32
%
Root Mean Square 78,805.45
Adjusted
R-Square
97.10
%
Ljung-Box
7.63
Root Mean Square
Error
12,897.71
Method Statistics
Value
Method
Selected
Event Model
Basic Method Holt Winters
Level (for Event Index) 0.20
Level 0.05
Seasonal
1.00
Trend 0.00
Decomposition type
Multiplicative
Seasonal
Indexes
Value
Event Indexes Value
Index 1 1.00
Index 1 1.01
Index 2 1.32
Index 2 1.00
Index 3 1.32
Index 3 1.06
Index 4 1.45
Index 4 1.03
Index 5 1.01
Index 5 0.94
Index 6 0.99
Index 6 0.99
Index 7 0.83
Index 8 0.78
Index 9 0.86
Index
10
0.87
Index
11
0.75
Index
12
0.82
Consider the ForecastX printout above. This is the forecast for
a manufactured product.
1. A)
This is a Winter’s Exponential Smoothing model.
2. B)
This is a Holt’s Smoothing model.
3. C)
This is an Event model.
4. D)
This is a Simple Smoothing model.
61) Smoothing 2
Accuracy
Measures
Value
Forecast
Statistics
Value
AIC
593.72
Durbin Watson (12) 0.66
BIC
597.61
Mean 351,007.33
MAPE
1.84
%
Standard Deviation 80,306.64
R-Square
97.32
%
Root Mean Square
78,805.45
Adjusted
R-Square
97.10
%
Ljung-Box
7.63
Root Mean Square
Error
12,897.71
Method
Statistics
Value
Method
Selected
Event Model
Basic Method Holt Winters
Level (for Event Index) 0.20
Level 0.05
Seasonal
1.00
Trend 0.00
Decomposition type
Multiplicative
Seasonal
Indexes
Value
Event Indexes Value
Index 1 1.00
Index 1 1.01
Index 2 1.32
Index 2 1.00
Index 3 1.32
Index 3 1.06
Index 4 1.45
Index 4 1.03
Index 5 1.01
Index 5 0.94
Index 6 0.99
Index 6 0.99
Index 7 0.83
Index 8 0.78
Index 9 0.86
Index
10
0.87
Index
11
0.75
Index
12
0.82
Consider the ForecastX printout above.
1. A)
There is little trend in the data.
2. B)
There is clear seasonality in the data.
3. C)
The event indices show some (but small) promotional effect.
4. D)
All of the options are correct.
62) Smoothing 2
Accuracy
Measures
Value
Forecast Statistics
Value
AIC
593.72
Durbin Watson (12) 0.66
BIC
597.61
Mean 351,007.33
MAPE
1.84
%
Standard Deviation 80,306.64
R-Square
97.32
%
Root Mean Square
78,805.45
Adjusted
R-Square
97.10
%
Ljung-Box
7.63
Root Mean Square Error
12,897.71
Method
Statistics
Value
Method
Selected
Event Model
Basic Method Holt Winters
Level (for Event Index) 0.20
Level 0.05
Seasonal
1.00
Trend 0.00
Decomposition type Multiplicative
Seasonal
Indexes
Value
Event Indexes Value
Index 1 1.00
Index 1 1.01
Index 2 1.32
Index 2 1.00
Index 3 1.32
Index 3 1.06
Index 4 1.45
Index 4 1.03
Index 5 1.01
Index 5 0.94
Index 6 0.99
Index 6 0.99
Index 7 0.83
Index 8 0.78
Index 9 0.86
Index
10
0.87
Index
11
0.75
Index
12
0.82
Consider the ForecastX printout above. The seasonal index 4 has
a value of 1.45. This indicates
1. A)
that sales in period 4 are usually below average.
2. B)
that sales in period 4 are usually above average.
3. C)
that sales in period 4 are usually quite close to the period average.
4. D)
that sales in period 4 have no seasonal effect.
63) Smoothing 2
Accuracy
Measures
Value
Forecast
Statistics
Value
AIC
593.72
Durbin Watson (12) 0.66
BIC
597.61
Mean 351,007.33
MAPE
1.84
%
Standard Deviation 80,306.64
R-Square
97.32
%
Root Mean Square
78,805.45
Adjusted
R-Square
97.10
%
Ljung-Box
7.63
Root Mean Square
Error
12,897.71
Method
Statistics
Value
Method
Selected
Event Model
Basic Method Holt Winters
Level (for Event Index) 0.20
Level 0.05
Seasonal
1.00
Trend 0.00
Decomposition type
Multiplicative
Seasonal Indexes
Value
Event Indexes Value
Index 1 1.00
Index 1 1.01
Index 2 1.32
Index 2 1.00
Index 3 1.32
Index 3 1.06
Index 4 1.45
Index 4 1.03
Index 5 1.01
Index 5 0.94
Index 6 0.99
Index 6 0.99
Index 7 0.83
Index 8 0.78
Index 9 0.86
Index 10
0.87
Index
11
0.75
Index
12
0.82
The Trend factor above is given as 0.00.
1. A)
This indicates that there is little (or no) seasonality.
2. B)
This indicates that there is little (or no) trend.
3. C)
This indicates that the events have little (or no) effect on sales.
4. D)
This indicates that the model has little (or no) explanatory power.
64) Smoothing 2
Accuracy
Measures
Value
Forecast Statistics
Value
AIC
593.72
Durbin Watson (12) 0.66
BIC
597.61
Mean 351,007.33
MAPE
1.84
%
Standard Deviation 80,306.64
R-Square
97.32
%
Root Mean Square
78,805.45
Adjusted
R-Square
97.10
%
Ljung-Box
7.63
Root Mean Square
Error
12,897.71
Method Statistics
Value
Method
Selected
Event Model
Basic Method Holt Winters
Level (for Event Index) 0.20
Level 0.05
Seasonal
1.00
Trend 0.00
Decomposition type
Multiplicative
Seasonal
Indexes
Value
Event Indexes Value
Index 1 1.00
Index 1 1.01
Index 2 1.32
Index 2 1.00
Index 3 1.32
Index 3 1.06
Index 4 1.45
Index 4 1.03
Index 5 1.01
Index 5 0.94
Index 6 0.99
Index 6 0.99
Index 7 0.83
Index 8 0.78
Index 9 0.86
Index
10
0.87
Index
11
0.75
Index
12
0.82
In the ForecastX model presented above,
1. A)
all of the events appear to contribute positively to sales.
2. B)
some of the events appear to contribute negatively to sales.
3. C)
none of the events appear to contribute negatively to sales
4. D)
none of the events appear to contribute positively to sales.
65) In event models,
1. A)
events are analogous to seasons in a seasonal model.
2. B)
events need not be defined in the forecast period.
3. C)
the researcher is unable to specify the underlying model.
4. D)
“load” and “deload” factors are never used.
66) Winters
Accuracy
Measures
Value
Forecast
Statistics
Value
AIC
613.59
Durbin Watson (12) 1.76
BIC
617.48
Mean 351,007.33
MAPE
2.83
%
Standard Deviation 80,306.64
R-Square
94.41
%
Root Mean Square
78,805.45
Adjusted
R-Square
93.94
%
Ljung-Box
2.74
Root Mean Square
Error
18,634.24
Method
Statistics
Value
Method
Selected
Holt Winters
Level 0.08
Seasonal
1.00
Trend 0.00
Decomposition type
Multiplicative
Seasonal
Indexes
Value
Index 1 1.00
Index 2 1.32
Index 3 1.31
Index 4 1.45
Index 5 1.01
Index 6 0.99
Index 7 0.83
Index 8 0.78
Index 9 0.86
Index
10
0.87
Index
11
0.74
Index
12
0.83
Consider the Audit Trail statistics for a Winters model above.
1. A)
The Trend value of 0.00 indicates trend is present.
2. B)
The Trend value of 0.00 indicates seasonality is present.
3. C)
The Trend value of 0.00 indicates no trend is present.
4. D)
The Trend value of 0.00 indicates no seasonality is present.
67) Winters
Accuracy
Measures
Value
Forecast
Statistics
Value
AIC
613.59
Durbin Watson (12) 1.76
BIC
617.48
Mean 351,007.33
MAPE
2.83
%
Standard Deviation 80,306.64
R-Square
94.41
%
Root Mean Square
78,805.45
Adjusted
R-Square
93.94
%
Ljung-Box
2.74
Root Mean Square
Error
18,634.24
Method Statistics
Value
Method
Selected
Holt Winters
Level 0.08
Seasonal
1.00
Trend 0.00
Decomposition type
Multiplicative
Seasonal
Indexes
Value
Index 1 1.00
Index 2 1.32
Index 3 1.31
Index 4 1.45
Index 5 1.01
Index 6 0.99
Index 7 0.83
Index 8 0.78
Index 9 0.86
Index
10
0.87
Index
11
0.74
Index
12
0.83
In the Winters smoothing model above,
1. A)
the Seasonal value of 1.00 indicates a high degree of seasonality is present.
2. B)
the Seasonal value of 1.00 indicates a low degree of seasonality is present.
3. C)
the Seasonal value of 1.00 indicates that the trend is positive.
4. D)
None of the options are true.
68) Winters
Accuracy
Measures
Value
Forecast
Statistics
Value
AIC
613.59
Durbin Watson (12) 1.76
BIC
617.48
Mean 351,007.33
MAPE
2.83
%
Standard Deviation 80,306.64
R-Square
94.41
%
Root Mean Square
78,805.45
Adjusted
R-Square
93.94
%
Ljung-Box
2.74
Root Mean Square
Error
18,634.24
Method
Statistics
Value
Method
Selected
Holt Winters
Level 0.08
Seasonal
1.00
Trend 0.00
Decomposition type
Multiplicative
Seasonal
Indexes
Value
Index 1 1.00
Index 2 1.32
Index 3 1.31
Index 4 1.45
Index 5 1.01
Index 6 0.99
Index 7 0.83
Index 8 0.78
Index 9 0.86
Index
10
0.87
Index
11
0.74
Index
12
0.83
In the Winters model shown above, index 1 refers to calendar
month 1 in the data.
1. A)
Thus, calendar month 3 is a below average month.
2. B)
Thus, calendar month 3 is an above average month.
3. C)
Thus, calendar month 3 is an average month.
4. D)
Nothing can be deduced about calendar month 3.
69) Winters
Accuracy
Measures Value
Forecast Statistics
Value
AIC
613.59
Durbin Watson (12) 1.76
BIC
617.48
Mean 351,007.33
MAPE 2.83
%
Standard Deviation 80,306.64
R-Square
94.41
%
Root Mean Square
78,805.45
Adjusted
R-Square
93.94
%
Ljung-Box
2.74
Root Mean Square
Error
18,634.24
Method
Statistics
Value
Method
Selected
Holt Winters
Level 0.08
Seasonal
1.00
Trend 0.00
Decomposition type
Multiplicative
Seasonal
Indexes
Value
Index 1 1.00
Index 2 1.32
Index 3 1.31
Index 4 1.45
Index 5 1.01
Index 6 0.99
Index 7 0.83
Index 8 0.78
Index 9 0.86
Index
10
0.87
Index
11
0.74
Index
12
0.83
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