Unformatted text preview: cts, if the price of each product is
weighted by the quantity of the product purchased in a given period of time, the resulting
index is called ___________ price index.
A. Paasche
B. Weighted aggregate
C. Laspeyres
D. Cyclical (seasonal) 76. _______________ index is most useful if the base quantities provide a reasonable
representation of consumption patterns in succeeding time periods.
A. Paasche
B. Weighted aggregate
C. Laspeyres
D. Cyclical (seasonal) 77. In the multiplicative decomposition method, the centered moving averages provide an
estimate of trend x _____.
A. index
B. cycle
C. seasonal
D. irregular 11538 Chapter 01  An Introduction to Business Statistics 78. A positive autocorrelation implies that negative error terms will be followed by
_________ error terms.
A. negative
B. positive
C. either negative or positive
D. irregular 79. A simple index is computed by using the values of one time series, while the _______
index is based on a "market basket" consisting of more than one time series.
A. weighted
B. aggregate
C. cyclical
D. trend 80. The Laspeyres index and the Paasche index are both examples of _________ aggregate
price indexes.
A. irregular
B. cyclical
C. trend
D. weighted 81. The Consumer Price Index and the Producer Price Index are both calculated using the
_________ index formula.
A. Paasche
B. Weighted aggregate
C. Laspeyres
D. Cyclical (seasonal) 82. The ________ index is a weighted aggregate price index that uses the base period
quantities as weights in all succeeding time periods.
A. Paasche
B. Firstorder autocorrelation
C. Laspeyres
D. Cyclical (seasonal) 11539 Chapter 01  An Introduction to Business Statistics 83. The ________ index is a weighted aggregate price index. It is accurate in its calculation of
the periodic prices, however using this index it is difficult to compare the prices in different
time periods.
A. Paasche
B. Firstorder autocorrelation
C. Laspeyres
D. Cyclical (seasonal) 84. Given the following data Compute the total error (sum of the error terms).
A. 175
B. 15
C. 15
D. 225 11540 Chapter 01  An Introduction to Business Statistics 85. Given the following data Compute the mean squared error (deviation).
A. 225
B. 28.1
C. 496.9
D. 3975 86. Given the following data Compute the mean absolute deviation (MAD).
A. 21.9
B. 175
C. 225
D. 28.1 11541 Chapter 01  An Introduction to Business Statistics 87. Given the following data Compute the total error (sum of the error terms).
A. 60
B. 10
C. 10
D. 100 88. Given the following data Compute the mean squared deviation (error).
A. 100
B. 125
C. 750
D. 16.7 11542 Chapter 01  An Introduction to Business Statistics 89. Given the following data Compute the mean absolute deviation.
A. 60
B. 10
C. 10
D. 16.7 90. Consider the following data: Calculate S0 using simple exponential smoothing and α = 2.
A. 19.5
B. 19
C. 18.6
D. 19.25 11543 Chapter 01  An Introduction to Business Statistics 91. Consider the following data: Calculate S1 using simple exponential smoothing and α = 2.
A. 19
B. 17.4
C. 19.6
D. 18.6 11544 Chapter 01  An Introduction to Business Statistics 92. Consider the following data: Use simple exponential smoothing with α = 0.2.and determine the forecast error for time
period 1.
A. 1.6
B. 1.6
C. 0
D. 2 11545 Chapter 01  An Introduction to Business Statistics 93. Consider the following data: Consider the following data and calculate S3 using simple exponential smoothing if S1 = 18.6
and α = 0.2.
A. 19.08
B. 19.00
C. 19.60
D. 19.06 11546 Chapter 01  An Introduction to Business Statistics 94. Consider the following data: Calculate S5 using simple exponential smoothing if S3 = 19.064 and α = 0.2.
A. 19.48
B. 19.85
C. 18.80
D. 19.80 95. Based on the following data, a forecaster used simple exponential smoothing and
determined the following: S0 = 19, S1 = 18.6, S2 = 19.08, S3 = 19.064, S4 = 19.851 and S5 =
19.481. Calculate the average forecast error.
A. 3.0
B. 1.80
C. 1.643
D. 1.924 11547 Chapter 01  An Introduction to Business Statistics 96. Based on the following data, a forecaster used simple exponential smoothing and
determined the following: S0 = 19, S1 = 18.6, S2 = 19.08, S3 = 19.064, S4 = 19.851 and S5 =
19.481. Calculate the Mean Squared Deviation (MSD or MSE).
A. 18.355
B. 3.671
C. 13.494
D. 6.314 97. Based on the following data, a forecaster used simple exponential smoothing and
determined the following: S0 = 19, S1 = 18.6, S2 = 19.08, S3 = 19.064, S4 = 19.851 and S5 =
19.481. Calculate the Mean Absolute Deviation (MAD).
A. 3.671
B. 8.215
C. 2.161
D. 1.643 11548 Chapter 01  An Introduction to Business Statistics 98. Consider the following data and calculate S1 using simple exponential smoothing and =
0.3. A. 19.14
B. 19.00
C. 18.40
D. 18.55 99. Consider the following data and calculate S2 using simple exponential smoothing and α =
0.3. A. 18.40
B. 19.18
C. 19.00
D. 19.60 11549 Chapter 01  An Introduction to Business Statistics 100. Consider the following data and calculations. Calculate the estimated value of b1 and b0
an...
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