Deck 15: Time-Series Forecasting and Index Numbers
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Deck 15: Time-Series Forecasting and Index Numbers
1
Forecast error is the difference between the value of the response variable and those of the explanatory variables.
False
2
Naïve forecasting models have no useful applications because they do not take into account data trend,cyclical effects or seasonality.
False
3
Two popular general categories of smoothing techniques are averaging models and exponential models.
True
4
One of the main techniques for isolating the effects of seasonality is reconstitution.
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5
If the trend equation is linear in time,the slope indicates the increase,or decrease when negative,in the forecasted value of the response value Y for the next time period.
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6
When a trucking firm uses the number of shipments for January of the previous year as the forecast for January next year,it is using a naïve forecasting model.
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7
If autocorrelation occurs in regression analysis,then the confidence intervals and tests using the t and F distributions are no longer strictly applicable.
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8
One of the main techniques for isolating the effects of seasonality is decomposition.
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9
An exponential smoothing technique in which the smoothing constant alpha is equal to one is equivalent to a regression forecasting model.
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10
Although seasonal effects can confound a trend analysis,a regression model is robust to these effects and the researcher does not need to adjust for seasonality prior to using a regression model to analyze trends.
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11
Time-series data are data gathered on a desired characteristic at a particular point in time.
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12
If the trend equation is quadratic in time t=1….T,the forecast value for the next time T+1 depends on time T.
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13
Linear regression models cannot be used to analyze quadratic trends in time-series data.
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14
Two popular general categories of smoothing techniques are exponential models and logarithmic models.
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15
One of the ways to overcome the autocorrelation problem in a regression forecasting model is to increase the level of significance for the F test
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16
A stationary time-series data has only trend but no cyclical or seasonal effects.
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17
The long-term general direction of data is referred to as series.
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18
When the error terms of a regression forecasting model are correlated the problem of autocorrelation occurs.
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19
One of the ways to overcome the autocorrelation problem in a regression forecasting model is to transform the variables by taking the first-differences.
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20
For large datasets,the mean error (ME)and mean absolute deviation (MAD)always have the same numerical value.
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21
Using a three-month moving average,the forecast value for October made at the end of September in the following time series would be ____________. 
A) 11.60
B) 10.00
C) 9.07
D) 8.06
E) 9.67

A) 11.60
B) 10.00
C) 9.07
D) 8.06
E) 9.67
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22
Autocorrelation in a regression forecasting model can be detected by the F test.
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23
Using a three-month moving average (with weights of 6,3,and 1 for the most current value,next most current value and oldest value,respectively),the forecast value for October made at the end of September in the following time series would be__________. 
A) 11.60
B) 10.00
C) 9.67
D) 8.60
E) 6.11

A) 11.60
B) 10.00
C) 9.67
D) 8.60
E) 6.11
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24
A time series with forecast values and error terms is presented in the following table. The mean absolute deviation (MAD)for this forecast is ___________. 
A) 3.54
B) 7.41
C) 4.43
D) 17.72
E) 4.34

A) 3.54
B) 7.41
C) 4.43
D) 17.72
E) 4.34
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25
Unweighted price indexes compare across the entire time period for which there is data.
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26
A time series analysis was performed to determine the number of new online customers that joined the 'Jelly of the Month Club'. The actual number of new customers,the forecast values and error terms is presented in the following table.The mean absolute deviation (MAD)for this forecast is ___________. 
A) -0.50
B) 0.50
C) 1.50
D) 7.00
E) 3.00

A) -0.50
B) 0.50
C) 1.50
D) 7.00
E) 3.00
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27
Using a three-month moving average,the forecast value for November in the following time series is ____________. 
A) 11.60
B) 10.00
C) 9.67
D) 8.60
E) 6.00

A) 11.60
B) 10.00
C) 9.67
D) 8.60
E) 6.00
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28
In exponential smoothing models,the value of the smoothing constant may be any number between ___________.
A) -1 and 1
B) -5 and 5
C) 0 and 1
D) 0 and 10
E) 0 and 100
A) -1 and 1
B) -5 and 5
C) 0 and 1
D) 0 and 10
E) 0 and 100
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29
A time series with forecast values and error terms is presented in the following table. The mean error (ME)for this forecast is ___________. 
A) 1.67
B) 1.34
C) 6.68
D) 3.67
E) 2.87

A) 1.67
B) 1.34
C) 6.68
D) 3.67
E) 2.87
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30
Using a three-month moving average (with weights of 6,3,and 1 for the most current value,next most current value and oldest value,respectively),the forecast value for November in the following time series is ____________. 
A) 11.60
B) 10.00
C) 9.67
D) 8.06
E) 8.60

A) 11.60
B) 10.00
C) 9.67
D) 8.06
E) 8.60
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31
When forecasting with exponential smoothing,data from previous periods is _________.
A) given equal importance
B) given exponentially increasing importance
C) ignored
D) given exponentially decreasing importance
E) linearly decreasing importance
A) given equal importance
B) given exponentially increasing importance
C) ignored
D) given exponentially decreasing importance
E) linearly decreasing importance
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32
A time series analysis was performed to determine the number of new online customers that joined the 'Jelly of the Month Club'. The actual number of new customers,the forecast values and error terms is presented in the following table.The mean squared error (MSE)for this forecast is ___________. 
A) -0.50
B) 0.50
C) 1.50
D) 7.00
E) 3.00

A) -0.50
B) 0.50
C) 1.50
D) 7.00
E) 3.00
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33
Use of a smoothing constant value greater than 0.5 in an exponential smoothing model gives more weight to ___________.
A) the actual value for the current period
B) the actual value for the previous period
C) the forecast for the current period
D) the forecast for the previous period
E) the forecast for the next period
A) the actual value for the current period
B) the actual value for the previous period
C) the forecast for the current period
D) the forecast for the previous period
E) the forecast for the next period
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34
The city golf course is interested in starting a junior golf program.The golf pros has collected data on the number of youths under 13 that have played golf during the last 4 months.Using a three-month moving average,the forecast value for October made at the end of September in the following time series would be ____________. 
A) 24
B) 21
C) 21.56
D) 19.22
E) 22

A) 24
B) 21
C) 21.56
D) 19.22
E) 22
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35
A time series with forecast values and error terms is presented in the following table. The mean squared error (MSE)for this forecast is ___________. 
A) 13.33
B) 17.94
C) 89.71
D) 22.43
E) 32.34

A) 13.33
B) 17.94
C) 89.71
D) 22.43
E) 32.34
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36
The city golf course is interested in starting a junior golf program.The golf pro has collected data on the number of youths under 13 that have played golf during the last 4 months.Using a three-month moving average,the forecast value for November in the following time series would be ____________. 
A) 24
B) 21
C) 21.56
D) 19.22
E) 22

A) 24
B) 21
C) 21.56
D) 19.22
E) 22
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37
Use of a smoothing constant value less than 0.5 in an exponential smoothing model gives more weight to ___________.
A) the actual value for the current period
B) the actual value for the previous period
C) the forecast for the current period
D) the forecast for the previous period
E) the forecast for the next period
A) the actual value for the current period
B) the actual value for the previous period
C) the forecast for the current period
D) the forecast for the previous period
E) the forecast for the next period
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38
In statistics,the Winters' Three Parameter statistic is a test statistic used to detect the presence of autocorrelation in the residuals from a regression analysis.
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39
A small value of the Durbin-Watson statistic indicates that successive error terms are positively correlated.
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40
A time series analysis was performed to determine the number of new online customers that joined the 'Jelly of the Month Club'. The actual number of new customers,the forecast values and error terms is presented in the following table. The mean error (ME)for this forecast is ___________. 
A) -0.50
B) 0.50
C) 1.50
D) 7.00
E) 3.00

A) -0.50
B) 0.50
C) 1.50
D) 7.00
E) 3.00
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41
The following graph of a time-series data suggests a _______________ trend. 
A) linear
B) quadratic
C) cosine
D) tangential
E) flat

A) linear
B) quadratic
C) cosine
D) tangential
E) flat
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42
In an autoregressive forecasting model,the independent variable(s)is (are)______.
A) time-lagged values of the dependent variable
B) first-order differences of the dependent variable
C) second-order, or higher, differences of the dependent variable
D) first-order quotients of the dependent variable
E) time-lagged values of the independent variable
A) time-lagged values of the dependent variable
B) first-order differences of the dependent variable
C) second-order, or higher, differences of the dependent variable
D) first-order quotients of the dependent variable
E) time-lagged values of the independent variable
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43
Jim Royo,manager of Billings Building Supply (BBS),wants to develop a model to forecast BBS's monthly sales (in $1,000's). He selects the dollar value of residential building permits (in $10,000)as the predictor variable. An analysis of the data yielded the following tables.
Using = 0.05 the critical value of the Durbin-Watson statistic,dL, is _________.
A) 1.24
B) 1.22
C) 1.13
D) 1.15
E) 1.85


Using = 0.05 the critical value of the Durbin-Watson statistic,dL, is _________.
A) 1.24
B) 1.22
C) 1.13
D) 1.15
E) 1.85
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44
The ratios of "actuals to moving averages" (seasonal indexes)for a time series are presented in the following table as percentages.
The final (completely adjusted)estimate of the seasonal index for Q1 is __________.
A) 109.733
B) 109.921
C) 113.853
D) 113.492
E) 111.545

A) 109.733
B) 109.921
C) 113.853
D) 113.492
E) 111.545
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45
The forecast value for September 21.1 and the actual value turned out to be 18.Using exponential smoothing with = 0.30,the forecast value for October would be ______.
A) 18.09
B) 18.93
C) 20.17
D) 21.00
E) 17.07
A) 18.09
B) 18.93
C) 20.17
D) 21.00
E) 17.07
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46
What is the forecast for the Period 7 using a 3-period moving average technique,given the following time-series data for six past periods? 
A) 164.67
B) 156.00
C) 148.00
D) 126.57
E) 158.67

A) 164.67
B) 156.00
C) 148.00
D) 126.57
E) 158.67
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47
Analysis of data for an autoregressive forecasting model produced the following tables.
The actual values of this time series,y,were 228,54,and 191 for May,June,and July,respectively. The predicted (forecast)value for August is __________.
A) -101.00
B) 104.54
C) 218.71
D) 21.56
E) -77.81


A) -101.00
B) 104.54
C) 218.71
D) 21.56
E) -77.81
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48
Analysis of data for an autoregressive forecasting model produced the following tables.
The results indicate that __________.
A) the first predictor, yt-1, is significant at the 10% level
B) the second predictor, yt-2, is significant at the 1% level
C) all predictor variables are significant at the 5% level
D) none of the predictor variables are significant at the 5% level
E) the overall regression model is not significant at 5% level


A) the first predictor, yt-1, is significant at the 10% level
B) the second predictor, yt-2, is significant at the 1% level
C) all predictor variables are significant at the 5% level
D) none of the predictor variables are significant at the 5% level
E) the overall regression model is not significant at 5% level
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49
The forecast value for August was 22 and the actual value turned out to be 19.Using exponential smoothing with = 0.30,the forecast value for September would be ______.
A) 21.1
B) 19.9
C) 18.1
D) 22.9
E) 21.0
A) 21.1
B) 19.9
C) 18.1
D) 22.9
E) 21.0
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50
Fitting a linear trend to 36 monthly data points (January 2011 = 1,February 2011 =2,March 2011 = 3,etc.)produced the following tables.
The projected trend value for January 2014 is ________.
A) 544.29
B) 868.61
C) 652.39
D) 760.50
E) 876.90


A) 544.29
B) 868.61
C) 652.39
D) 760.50
E) 876.90
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51
Calculating the "ratios of actuals to moving average" is a common step in time series decomposition. The results (the quotients)of this step estimate the ________.
A) trend and cyclical components
B) seasonal and irregular components
C) cyclical and irregular components
D) trend and seasonal components
E) irregular components
A) trend and cyclical components
B) seasonal and irregular components
C) cyclical and irregular components
D) trend and seasonal components
E) irregular components
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52
The city golf course is interested in starting a junior golf program.The golf pro has collected data on the number of youths under 13 that have played golf during the last 4 months.Using a three-month moving average (with weights of 5,3,and 1 for the most current value,next most current value and oldest value,respectively),the forecast value for November in the following time series would be ____________. 
A) 24
B) 21
C) 21.56
D) 19.22
E) 22

A) 24
B) 21
C) 21.56
D) 19.22
E) 22
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53
Analysis of data for an autoregressive forecasting model produced the following tables.
The forecasting model is __________.
A) yt = 3.745787 + 0.082849yt-1 + 0.035709yt-2
B) yt = 3.85094 + 0.70434yt-1 - 0.62669yt-2
C) yt = 0.84426 - 1.66023yt-1 + 14.65023yt-2
D) yt = 0.34299 + 0.13822yt-1 + 9.69yt-2
E) yt = 0.34299 + 0.13822yt-1 - 6.69yt-2


A) yt = 3.745787 + 0.082849yt-1 + 0.035709yt-2
B) yt = 3.85094 + 0.70434yt-1 - 0.62669yt-2
C) yt = 0.84426 - 1.66023yt-1 + 14.65023yt-2
D) yt = 0.34299 + 0.13822yt-1 + 9.69yt-2
E) yt = 0.34299 + 0.13822yt-1 - 6.69yt-2
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54
Fitting a linear trend to 36 monthly data points (January 2011 = 1,February 2011 =2,March 2011 = 3,etc.)produced the following tables.
The projected trend value for January 2014 is ________.
A) 231.39
B) 555.71
C) 339.50
D) 447.76
E) 355.71


A) 231.39
B) 555.71
C) 339.50
D) 447.76
E) 355.71
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55
The city golf course is interested in starting a junior golf program.The golf pro has collected data on the number of youths under 13 that have played golf during the last 4 months.Using a three-month moving average (with weights of 5,3,and 1 for the most current value,next most current value and oldest value,respectively),the forecast value for October made at the end of September in the following time series would be __________. 
A) 24
B) 21
C) 21.56
D) 19.22
E) 22

A) 24
B) 21
C) 21.56
D) 19.22
E) 22
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56
Analysis of data for an autoregressive forecasting model produced the following tables.
The actual values of this time series,y,were 228,54,and 191 for May,June,and July,respectively.The forecast value predicted by the model for July is __________.
A) -101.00
B) 104.54
C) 218.71
D) 21.56
E) -77.81


A) -101.00
B) 104.54
C) 218.71
D) 21.56
E) -77.81
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57
The high and low values of the "ratios of actuals to moving average" are ignored when finalizing the seasonal index for a period (month or quarter)in time series decomposition.The rationale for this is to ________.
A) reduce the sample size
B) eliminate autocorrelation
C) minimize serial correlation
D) eliminate the irregular component
E) eliminate the trend
A) reduce the sample size
B) eliminate autocorrelation
C) minimize serial correlation
D) eliminate the irregular component
E) eliminate the trend
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58
The following graph of a time-series data suggests a _______________ trend. 
A) linear
B) tangential
C) cosine
D) quadratic
E) flat

A) linear
B) tangential
C) cosine
D) quadratic
E) flat
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59
The following graph of time-series data suggests a _______________ trend. 
A) linear
B) quadratic
C) cosine
D) tangential
E) flat

A) linear
B) quadratic
C) cosine
D) tangential
E) flat
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60
Which of the following is not a component of time series data?
A) Trend
B) Seasonal fluctuations
C) Cyclical fluctuations
D) Normal fluctuations
E) Irregular fluctuations
A) Trend
B) Seasonal fluctuations
C) Cyclical fluctuations
D) Normal fluctuations
E) Irregular fluctuations
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61
A time series with forecast values is presented in the following table:
If the mean square error (MAD)is 257,then a = ______.
A) 4283.33
B) 428.33
C) 15.42
D) 42.833
E) 1.542

A) 4283.33
B) 428.33
C) 15.42
D) 42.833
E) 1.542
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62
Weighted aggregate price indexes are also known as _______.
A) unbalanced indexes
B) balanced indexes
C) value indexes
D) multiplicative indexes
E) overall indexes
A) unbalanced indexes
B) balanced indexes
C) value indexes
D) multiplicative indexes
E) overall indexes
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63
A time series with forecast values is presented in the following table:
On this table,a is some nondisclosed value.The mean square error (MSE)is ______% of a.
A) 4.15
B) 0.415
C) 4.15a
D) 0.415a
E) 0.00332

A) 4.15
B) 0.415
C) 4.15a
D) 0.415a
E) 0.00332
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64
When constructing a weighted aggregate price index,the weights usually are _____.
A) prices of substitute items
B) prices of complementary items
C) quantities of the respective items
D) squared quantities of the respective items
E) quality of individual items
A) prices of substitute items
B) prices of complementary items
C) quantities of the respective items
D) squared quantities of the respective items
E) quality of individual items
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65
A weighted aggregate price index where the weight for each item is computed by using the quantities of the base period is known as the a.Paasche Index
B)Simple Index
C)Laspeyres Index
D)Consumer Price index
E)Producer Price index
B)Simple Index
C)Laspeyres Index
D)Consumer Price index
E)Producer Price index
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66
A time series with forecast values is presented in the following table:
If the mean absolute deviation (MAD)until September is 0.06,and the overall MAD is 0.05,
Then x = ______.
A) 1.270
B) 1.270 or 1.280
C) 1.265 or 1.285
D) 1.260 or 1.290
E) 1.285

Then x = ______.
A) 1.270
B) 1.270 or 1.280
C) 1.265 or 1.285
D) 1.260 or 1.290
E) 1.285
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67
Often,index numbers are expressed as ____________.
A) percentages
B) frequencies
C) cycles
D) regression coefficients
E) correlation coefficients
A) percentages
B) frequencies
C) cycles
D) regression coefficients
E) correlation coefficients
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68
Index numbers facilitate comparison of ____________.
A) means
B) data over time
C) variances
D) samples
E) deviations
A) means
B) data over time
C) variances
D) samples
E) deviations
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69
A time series with forecast values is presented in the following table:
If the mean square error (MSE)until September is 0.01125,and the overall MSE is 0.010125,
Then x = ______.
A) 1.15
B) 1.25
C) 1.3
D) 1.25 or 1.3
E) 1.2 or 1.35

Then x = ______.
A) 1.15
B) 1.25
C) 1.3
D) 1.25 or 1.3
E) 1.2 or 1.35
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70
Using 2011 as the base year,the 2010 value of the Laspeyres Price Index is ______. 
A) 69.92
B) 144.06
C) 100.21
D) 79.72
E) 99.72

A) 69.92
B) 144.06
C) 100.21
D) 79.72
E) 99.72
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71
Typically,the denominator used to calculate an index number is a measurement for the ____________ period.
A) base
B) current
C) spanning
D) intermediate
E) peak
A) base
B) current
C) spanning
D) intermediate
E) peak
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72
Using 2000 as the base year,the 1990 value of the Paasche' Price Index is ______. (Quantities are averages for the student body.) 
A) 80.72
B) 162.28
C) 240.06
D) 50.45
E) 30.35

A) 80.72
B) 162.28
C) 240.06
D) 50.45
E) 30.35
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73
Jim Royo,manager of Billings Building Supply (BBS),wants to develop a model to forecast BBS's monthly sales (in $1,000's).He selects the dollar value of residential building permits (in $10,000)as the predictor variable.An analysis of the data yielded the following tables.
Jim's calculated value for the Durbin-Watson statistic is 1.14. Using = 0.05,the appropriate decision is: _________.
A) do not reject H0: = 0
B) reject H0: = 0
C) do not reject H0: 0
D) the test is inconclusive
E) reject H0: ≠ 0


Jim's calculated value for the Durbin-Watson statistic is 1.14. Using = 0.05,the appropriate decision is: _________.
A) do not reject H0: = 0
B) reject H0: = 0
C) do not reject H0: 0
D) the test is inconclusive
E) reject H0: ≠ 0
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74
The motivation for using an index number is to ________________.
A) transform the data to a standard normal distribution
B) transform the data for a linear model
C) eliminate bias from the sample
D) reduce data to an easier-to-use, more convenient form
E) reduce the variance in the data
A) transform the data to a standard normal distribution
B) transform the data for a linear model
C) eliminate bias from the sample
D) reduce data to an easier-to-use, more convenient form
E) reduce the variance in the data
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75
A weighted aggregate price index where the weight for each item is computed by using the quantities of the year of interest is known as the a.Paasche Index
B)Simple Index
C)Laspeyres Index
D)Consumer Price index
E)Producer Price index
B)Simple Index
C)Laspeyres Index
D)Consumer Price index
E)Producer Price index
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76
Using 2011 as the base year,the 2010 value of the Paasche' Price Index is ______. 
A) 99.79
B) 192.51
C) 100.29
D) 59.19
E) 39.99

A) 99.79
B) 192.51
C) 100.29
D) 59.19
E) 39.99
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77
A time series with forecast values is presented in the following table:
On this table,a is some nondisclosed value.The mean absolute deviation (MAD)
Is ______% of a.
A) 6a
B) 0.06a
C) 6
D) 0.06
E) 4.8

Is ______% of a.
A) 6a
B) 0.06a
C) 6
D) 0.06
E) 4.8
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78
Jim Royo,manager of Billings Building Supply (BBS),wants to develop a model to forecast BBS's monthly sales (in $1,000's). He selects the dollar value of residential building permits (in $10,000)as the predictor variable. An analysis of the data yielded the following tables.
Jim's calculated value for the Durbin-Watson statistic is 1.93. Using = 0.05,the appropriate decision is: _________.
A) do not reject H0: = 0
B) reject H0: ≠ 0
C) do not reject: 0
D) the test is inconclusive
E) reject H0: = 0


Jim's calculated value for the Durbin-Watson statistic is 1.93. Using = 0.05,the appropriate decision is: _________.
A) do not reject H0: = 0
B) reject H0: ≠ 0
C) do not reject: 0
D) the test is inconclusive
E) reject H0: = 0
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k this deck
79
Jim Royo,manager of Billings Building Supply (BBS),wants to develop a model to forecast BBS's monthly sales (in $1,000's). He selects the dollar value of residential building permits (in $10,000)as the predictor variable. An analysis of the data yielded the following tables.
Using = 0.05 the critical value of the Durbin-Watson statistic,dU, is _________.
A) 1.54
B) 1.42
C) 1.43
D) 1.44
E) 1.85


Using = 0.05 the critical value of the Durbin-Watson statistic,dU, is _________.
A) 1.54
B) 1.42
C) 1.43
D) 1.44
E) 1.85
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80
Using 2010 as the base year,the 2012 value of a simple price index for the following price data is _____________. 
A) 77.60
B) 114.13
C) 160.58
D) 99.30
E) 100.00

A) 77.60
B) 114.13
C) 160.58
D) 99.30
E) 100.00
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