1.The data below from the State Division of Motor Vehicles (DMV) shows the rate of new driver’s license applications.
| Month | Week1 | Application |
| April | 1 | 238 |
| 2 | 199 | |
| 3 | 215 | |
| 4 | 212 | |
| May | 1 | 207 |
| 2 | 211 | |
| 3 | 196 | |
| 4 | 206 |
Refer to Exhibit 17-5. Using weights of .4, .3, .2, and .1, what is the four-week weighted moving average forecast for April, week 1?
A) 204.1
2.Below are the first four values of a time series.
| Time Period | Time Series Value |
| 1 | 18 |
| 2 | 20 |
| 3 | 25 |
| 4 | 17 |
Using a four-period moving average, the forecasted value for period 5 is _.
A) 20
3.Consider the following time series:
| t | 1 | 2 | 3 | 4 |
| Yi | 4 | 7 | 9 | 10 |
Refer to Exhibit 17-2. The slope of linear trend equation, b1, is __.
A) 2.0
4.A method of smoothing a time series that can be used to identify the combined trend/cyclical component is
A) the moving average
5.If the estimate of the trend component is 158.2, the estimate of the seasonal component is 94%, the estimate of the cyclical component is 105%, and the estimate of the irregular component is 98%, then the multiplicative model will produce a forecast of _.
A) 153.02
6.If data for a time series analysis are collected on an annual basis only, which pattern can be ignored?
A) seasonal
7.To calculate an exponential smoothing forecast of demand, what values are required?
A) alpha, last forecast, last actual demand
8.A qualitative forecasting method that obtains forecasts through “group consensus” is known as the __.
A) Delphi approach
9.The data below from the State Division of Motor Vehicles (DMV) shows the rate of new driver’s license applications.
| Month | Week1 | Application |
| April | 1 | 238 |
| 2 | 199 | |
| 3 | 215 | |
| 4 | 212 | |
| May | 1 | 207 |
| 2 | 211 | |
| 3 | 196 | |
| 4 | 206 |
Refer to Exhibit 17-5. Using a three-week moving average, what is the forecast for the first week in April?
A) 204.33
10.Consider the following time series:
| Year (t) | Yi |
| 1 | 7 |
| 2 | 5 |
| 3 | 4 |
| 4 | 2 |
| 5 | 1 |
Refer to Exhibit 17-3. In which time period does the value of Yi reach 0?
A) 5.53
11.The term “exponential smoothing” comes from __.
A) the exponential nature of the weighting scheme used
12.Below are the first five values of a quarterly time series. The multiplicative model is appropriate and a four-quarter moving average will be used.
| Year | Quarter | Time Series Value Yt |
| 1 | 1 | 36 |
| 2 | 24 | |
| 3 | 16 | |
| 2 | 4 | 20 |
| 1 | 44 |
Refer to Exhibit 17-1. An estimate of the seasonal-irregular component for quarter 3 of year 1 is __.
A) 0.64
13.The forecasting method that is appropriate when the time series has no significant trend, cyclical, or seasonal effect is _.
A) moving averages
14.One measure of the accuracy of a forecasting model is the __.
A) mean absolute error
15.All of the following are true about a cyclical pattern EXCEPT it is __.
A) usually easier to forecast than a seasonal pattern due to less variability
16.A method that uses a weighted average of past values for arriving at smoothed time series values is known as __.
A) exponential smoothing
17.The forecasting model that makes use of the “least squares” method is __.
A) regression
18.One measure of the accuracy of a forecasting model is __.
A) the mean squared error
19.In situations where you need to compare forecasting methods for different time periods, the most appropriate accuracy measure is __.
A) mean absolute error
20.Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?
A) 1.0
21.Given a demand of 61, forecast of 58, and α of .3, what would the forecast for the next period be using simple exponential smoothing?
A) 58.9
22.The time series pattern that reflects a gradual shift or movement to a relatively higher or lower level over a longer time period is called the _.
A) trend pattern
23.Regarding a regression model, all of the following can be negative EXCEPT the __.
A) coefficient of determination
24.Which of the following forecasting methods puts the least weight on the most recent time series value?
A) exponential smoothing with α = .2
25.A group of observations measured at successive time intervals is known as a(n) _.
A) time series
26.The time series pattern that exists when the data fluctuate around a constant mean is the _.
A) horizontal pattern
27.Below are some values of a time series consisting of 26 time periods.
| Time Period | Time Series Value |
| 1 | 37 |
| 2 | 48 |
| 3 | 50 |
| 4 | 63 |
| . | |
| . | |
| . | |
| 23 | 105 |
| 24 | 107 |
| 25 | 112 |
| 26 | 114 |
The estimated regression equation for these data is
Yt = 16.23 + .52Y t–1 + .37Y t–2
The forecasted value for time period 27 is _.
A) 116.95
28.Below are the first four values of a time series.
| Time Period | Time Series Value |
| 1 | 18 |
| 2 | 20 |
| 3 | 25 |
| 4 | 17 |
Using a four-period moving average, the forecasted value for period 5 is _.
A) 20
29.Consider the following time series:
| t | 1 | 2 | 3 | 4 |
| Yi | 4 | 7 | 9 | 10 |
Refer to Exhibit 17-2. The slope of linear trend equation, b1, is __.
A) 2.0
30.Common types of data patterns that can be identified when examining a time series plot include all of the following EXCEPT __.
A) vertical
31.A component of the time series model that results in the multi-period above-trend and below-trend behavior of a time series is a(n) _.
A) cyclical component
32.Exhibit 17-5
The data below from the State Division of Motor Vehicles (DMV) shows the rate of new driver’s license applications.
| Month | Week1 | Application |
| April | 1 | 238 |
| 2 | 199 | |
| 3 | 215 | |
| 4 | 212 | |
| May | 1 | 207 |
| 2 | 211 | |
| 3 | 196 | |
| 4 | 206 |
Refer to Exhibit 17-5. Using weights of .6, .3, and .1, what is the three-week weighted moving average forecast for April, week 1?
A) 203.50
33.The time series pattern showing an alternating sequence of points below and above the trend line lasting more than one year is the _.
A) cyclical pattern
34.Using exponential smoothing, the demand forecast for time period 10 equals the demand forecast for time period 9 plus __.
A) α times (the error in the demand forecast for time period 9)
35.Consider the following time series:
| t | 1 | 2 | 3 | 4 |
| Yi | 4 | 7 | 9 | 10 |
Refer to Exhibit 17-2. The intercept, b0, is __.
A) 2.5
36.Exhibit 17-3
Consider the following time series:
| Year (t) | Yi |
| 1 | 7 |
| 2 | 5 |
| 3 | 4 |
| 4 | 2 |
| 5 | 1 |
Refer to Exhibit 17-3. In which time period does the value of Yi reach 0?
A) 5.53
37.The trend pattern is easy to identify by using _.
A) regression analysis
38.The objective of smoothing methods is to smooth out __.
A) random fluctuations
39.A seasonal pattern __.
A) can occur within a day
40.The following linear trend expression was estimated using a time series with 17 time periods.
Tt = 129.2 + 3.8t
The trend projection for time period 18 is _.
A) 197.6
41.The term “exponential smoothing” comes from __.
A) the exponential nature of the weighting scheme used
42.All of the following are true about a stationary time series EXCEPT __.
A) there is no variability in the time series over time
43.A parameter of the exponential smoothing model that provides the weight given to the most recent time series value in the calculation of the forecast value is known as the _.
A) smoothing constant
44.In the linear trend equation, T = b0 + b1t, b0 represents the _.
A) y-intercept
45.In the linear trend equation, Tt = b0 + b1t b1 represents the __.
A) slope of the trend line
46.Below are the first five values of a quarterly time series. The multiplicative model is appropriate and a four-quarter moving average will be used.
| Year | Quarter | Time Series Value Yt |
| 1 | 1 | 36 |
| 2 | 24 | |
| 3 | 16 | |
| 2 | 4 | 20 |
| 1 | 44 |
Refer to Exhibit 17-1. An estimate of the trend component times the cyclical component (T 2 C t) for quarter 3 of year 1, when a four-quarter moving average is used, is __.
A) 25
47.All of the following are true about time series methods EXCEPT __.
A) they identify a set of related independent, or explanatory, variables
48.Exhibit 17-3
Consider the following time series:
| Year (t) | Yi |
| 1 | 7 |
| 2 | 5 |
| 3 | 4 |
| 4 | 2 |
| 5 | 1 |
Refer to Exhibit 17-3. The slope of linear trend equation, b1, is __.
A) -1.5
49.Below are the first two values of a time series and the first two values of the exponential smoothing forecast.
Exponential Smoothing
| Time Period (t) | Time Series Value (Yt) | Forecast (Ft) |
| 1 | 18 | 18 |
| 2 | 22 | 18 |
If the smoothing constant equals .3, then the exponential smoothing forecast for time period 3 is _.
A) 19.2
50.Gradual shifting or movement of a time series to relatively higher or lower values over a longer period of time is called _.
A) a trend
51.For the following time series, you are given the moving average forecast.
| Time Period | Time Series Value | Moving Average Forecast |
| 1 | 23 | |
| 2 | 17 | |
| 3 | 17 | |
| 4 | 26 | 19 |
| 5 | 11 | 20 |
| 6 | 23 | 18 |
| 7 | 17 | 20 |
The mean squared error equals
A) 41
52.Exhibit 17-4
The Espresso Cart has had the following pattern of espresso sales over the last two weeks:
| Week1 | Week 2 | ||
| Monday | 873 | Monday | 912 |
| Tuesday | 904 | Tuesday | 859 |
| Wednesday | 911 | Wednesday | 906 |
| Thursday | 887 | Thursday | 900 |
| Friday | 899 | Friday | ? |
Refer to Exhibit 17-4. What is the forecast for Friday’s sales using a three-day weighted moving average with weights of .5 (for newest), .3, and .2 (for oldest)?
A) 893.6
53.Exhibit 17-2
Consider the following time series:
| t | 1 | 2 | 3 | 4 |
| Yi | 4 | 7 | 9 | 10 |
Refer to Exhibit 17-2. The forecast for period 5 is __.
A) 12.5
54.The following information on the seasonal-irregular component values is for a quarterly time series:
| Quarter | Seasonal-Irregular Component Values (StIt) |
| 1 | 1.23, 1.15, 1.16 |
| 2 | .86, .89, .83 |
| 3 | .77, .72, .79 |
| 4 | 1.20, 1.13, 1.17 |
The seasonal index for quarter 1 is __.
A) 1.18
55.Exhibit 17-2
Consider the following time series:
| t | 1 | 2 | 3 | 4 |
| Yi | 4 | 7 | 9 | 10 |
Refer to Exhibit 17-2. The forecast for period 10 is __.
A) 22.5
56.Exhibit 17-5
The data below from the State Division of Motor Vehicles (DMV) shows the rate of new driver’s license applications.
| Month | Week1 | Application |
| April | 1 | 238 |
| 2 | 199 | |
| 3 | 215 | |
| 4 | 212 | |
| May | 1 | 207 |
| 2 | 211 | |
| 3 | 196 | |
| 4 | 206 |
Refer to Exhibit 17-5. Using a five-week moving average, what is the forecast for the first week in April?
A) 206.40
57.The model that assumes that the actual time series value is the product of its components is the _.
A) multiplicative decomposition model
58.Exhibit 17-4
The Espresso Cart has had the following pattern of espresso sales over the last two weeks:
| Week1 | Week 2 | ||
| Monday | 873 | Monday | 912 |
| Tuesday | 904 | Tuesday | 859 |
| Wednesday | 911 | Wednesday | 906 |
| Thursday | 887 | Thursday | 900 |
| Friday | 899 | Friday | ? |
Refer to Exhibit 17-4. What is the forecast for Friday’s sales using a three-day moving average?
A) 888.33
59.Exhibit 17-2
Consider the following time series:
| t | 1 | 2 | 3 | 4 |
| Yi | 4 | 7 | 9 | 10 |
Refer to Exhibit 17-2. The intercept, b0, is __.
A) 2.5
60.Exhibit 17-3
Consider the following time series:
| Year (t) | Yi |
| 1 | 7 |
| 2 | 5 |
| 3 | 4 |
| 4 | 2 |
| 5 | 1 |
Refer to Exhibit 17-3. The forecast for period 10 is __.
A) -6.7
61.In situations where you need to compare forecasting methods for different time periods, the most appropriate accuracy measure is __.
A) mean absolute percentage error
62.All of the following are true about qualitative forecasting methods EXCEPT they __.
A) assume the pattern of the past will continue into the future
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