U.S. Civilian Labor Force

Answer each one of these test questions correctly. Please double check that answers are correct before returning. Thank you1
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Item 1
U.S. Civilian Labor Force (thousands)
Year Labor Force Year Labor Force
2007 168,954 2012 170,664
2008 169,691 2013 170,187
2009 168,147 2014 171,274
2010 168,686 2015 172,993
2011 169,031 2016 174,676


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(a) Make a line graph of the U.S. civilian labor force data.

Line Graph A Line Graph B Line Graph C Line Graph D

multiple choice
• Line Graph 1
• Line Graph 2
• Line Graph 3
• Line Graph 4

(b) Describe the trend (if any) and discuss possible causes.

Trend is positive Correct. There seems to be an increase Correctin the rate of growth over the past few years.

(d) Make forecasts using the following fitted trend models for years 2017-2019. (Round your answers to the nearest whole number.)

t Linear Quadratic Exponential
11
12
13


rev: 11_06_2019_QC_CS-189412, 05_28_2020_QC_CS-206768
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Item 2
U.S. Manufactured General Aviation Shipments, 1984–2016
Year Planes Year Planes Year Planes Year Planes
1984 3,861 1992 2,371 2000 4,246 2008 4,509
1985 3,459 1993 2,394 2001 4,064 2009 3,015

 1986            2,925           1994            2,358           2002            3,637           2010            2,764    
 1987            2,515           1995            2,507           2003            3,567           2011            2,753    
 1988            2,642           1996            2,545           2004            3,785           2012            2,946    
 1989            2,965           1997            2,979           2005            4,287           2013            3,045    
 1990            2,574           1998            3,630           2006            4,577           2014            3,061    
 1991            2,451           1999            3,934           2007            4,709           2015            3,022    

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(a) Plot the data on U.S. general aviation shipments.

Plot A Plot B Plot C Plot D

multiple choice
• Plot A Correct
• Plot B
• Plot C
• Plot D

(b) Describe the pattern and discuss possible causes. Hint: What economic factors affect major capital investments? (You may select more than one answer. Click the box with a check mark for the correct answer and double click to empty the box for the wrong answer.)

The factors affecting major capital investments:

check all that apply
• Business cyclesunanswered
• Interest ratesunanswered
• Foreign demandunanswered
• Forecastingunanswered

(c) Would a fitted trend be helpful? Explain.

No Correct, there is no clear Correcttrend over the past 30 years.

(d) Fit a moving average (e.g., period 2) to the data. Is it useful?

Yes Correct, a two year or three year moving average would be an acceptable Correctforecasting method for this time series.

(e) Make a forecast for 2016 using a method of your choice (including a judgment forecast). Justify your method. (Round your answer to the nearest whole number.)

The two year moving average forecast for 2016 is .
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Item 3
For each of the following fitted trends, make a prediction for period t = 17:

(a) yt = 2,286e0.076t (Do not round the intermediate calculations. Round your final answer to 1 decimal place.)

yˆy^

(b) yt = 1,149 + 12.78t (Do not round the intermediate calculations. Round your final answer to 1 decimal place.)

yˆy^

(c) yt = 501 + 18.2t – 7.1t2 (A negative value should be indicated by a minus sign. Do not round the intermediate calculations. Round your final answer to 1 decimal place.)

yˆy^

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Item 4
Daily Spot Exchange Rate, U.S. Dollars per Pound Sterling
Date Rate Date Rate Date Rate
1-Apr-04 1.8564 13-Apr-04 1.8160 23-Apr-04 1.7674
2-Apr-04 1.8293 14-Apr-04 1.7902 26-Apr-04 1.7857
5-Apr-04 1.8140 15-Apr-04 1.7785 27-Apr-04 1.7925
6-Apr-04 1.8374 16-Apr-04 1.8004 28-Apr-04 1.7720
7-Apr-04 1.8410 19-Apr-04 1.8055 29-Apr-04 1.7751
8-Apr-04 1.8325 20-Apr-04 1.7914 30-Apr-04 1.7744
9-Apr-04 1.8322 21-Apr-04 1.7720 3-May-04 1.7720
12-Apr-04 1.8358 22-Apr-04 1.7684 4-May-04 1.7907
5-May-04 1.7932 13-May-04 1.7584 21-May-04 1.7880
6-May-04 1.7941 14-May-04 1.7572 24-May-04 1.7908
7-May-04 1.7842 17-May-04 1.7695 25-May-04 1.8135
10-May-04 1.7723 18-May-04 1.7695 26-May-04 1.8142
11-May-04 1.7544 19-May-04 1.7827 27-May-04 1.8369
12-May-04 1.7743 20-May-04 1.7710 28-May-04 1.8330


Click here for the Excel Data File

(b) Perform simple exponential smoothing (using Excel’s Data Analysis or other software such as Minitab) using α = .05, .10, .20, and .50.

The degree of smoothing varies Correctdramatically as α is increased.

(c) Which value of α do you prefer? (Round your answers to 2 decimal places.)

α =

(d) Is exponential smoothing appropriate for this kind of data?

Yes Correct

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Item 5
Coca-Cola Revenues ($ millions), 2005–2010
Quarter 2005 2006 2007 2008 2009 2010
Qtr1 5,194 5,106 6,060 7,350 7,120 7,504
Qtr2 6,298 6,450 7,690 9,030 8,208 8,653
Qtr3 6,025 6,398 7,647 8,296 8,010 8,405
Qtr4 5,539 5,890 7,288 7,010 7,462 10,473


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(a-1) Use MegaStat or Minitab to deseasonalize Coca-Cola’s quarterly data. (Round your answers to 3 decimal places.)

 1   2   3   4

2005

2006

2007

2008

2009

2010

mean


(a-2) State the adjusted four quarterly indexes. (Round your answers to 3 decimal places.)

Q1 Q2 Q3 Q4


(a-3) What is the trend model for the deseasonalized time series? (Round your answers to 2 decimal places.)

yt = xt +

(b) State the model found when performing a regression using seasonal binaries. (A negative value should be indicated by a minus sign. Round your answers to 4 decimal places.)

(b) State the model found when performing a regression using seasonal binaries. (A negative value should be indicated by a minus sign. Round your answers to 4 decimal places.)

yt = + t + Q1 + Q2 + Q3

(c) Use the regression equation to make a prediction for each quarter in 2011. (Enter your answers in millions rounded to 3 decimal places.)

Quarter Predicted
Q1

Q2

Q3

Q4


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Item 6
JetBlue Airlines Revenue,
2008–2015 (millions)
Year Revenue Year Revenue
2008 3,388 2012 4,982
2009 3,286 2013 5,441
2010 3,779 2014 5,817
2011 4,504 2015 6,416


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(a) Use Excel, MegaStat, or MINITAB to fit both a linear and an exponential trend to the time series. (Round your answers to 2 decimal places.)

Linear yt = t +

Exponential yt = e t


(b) Make annual forecasts for 2016–2018, using the linear and exponential trend models. (Do not round the intermediate calculations. Round your final answers to 2 decimal places.)

t Linear Exponential
9

10

11


7
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Item 7
U.S. Civilian Labor Force (thousands)
Year Labor Force Year Labor Force
2007 153,918 2012 155,628
2008 154,655 2013 155,151
2009 153,111 2014 156,238
2010 153,650 2015 157,957
2011 153,995 2016 159,640


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(a) Make a line graph of the U.S. civilian labor force data.

Line Graph A Line Graph B Line Graph C Line Graph D

multiple choice
• Line Graph A Correct
• Line Graph B
• Line Graph C
• Line Graph D

(b) Describe the trend (if any) and discuss possible causes.

Trend is positive Correct. There seems to be an increase Correctin the rate of growth over the past few years.

(c) Fit three trend models: linear, exponential, and quadratic. Which model would offer the most believable forecasts? (You may select more than one answer. Click the box with a check mark for the correct answer and double click to empty the box for the wrong answer.)

check all that apply
• Linearunanswered
• Quadraticunanswered
• Exponentialunanswered

(d) Make forecasts using the following fitted trend models for years 2017-2019. (Round your answers to the nearest whole number.)

t Linear Quadratic Exponential
11
12
13


8
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Item 8
U.S. Manufactured General Aviation Shipments, 1984–2016
Year Planes Year Planes Year Planes Year Planes
1984 2,431 1992 941 2000 2,816 2008 3,079
1985 2,029 1993 964 2001 2,634 2009 1,585

 1986            1,495           1994            928         2002            2,207           2010            1,334    
 1987            1,085           1995            1,077           2003            2,137           2011            1,323    
 1988            1,212           1996            1,115           2004            2,355           2012            1,516    
 1989            1,535           1997            1,549           2005            2,857           2013            1,615    
 1990            1,144           1998            2,200           2006            3,147           2014            1,631    
 1991            1,021           1999            2,504           2007            3,279           2015            1,592    

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(a) Plot the data on U.S. general aviation shipments.

Plot A Plot B Plot C Plot D

multiple choice
• Plot A
• Plot B
• Plot C
• Plot D

(b) Describe the pattern and discuss possible causes. Hint: What economic factors affect major capital investments? (You may select more than one answer. Click the box with a check mark for the correct answer and double click to empty the box for the wrong answer.)

The factors affecting major capital investments:

check all that apply
• Business cyclesunanswered
• Interest ratesunanswered
• Foreign demandunanswered
• Forecastingunanswered

(c) Would a fitted trend be helpful? Explain.

No Correct, there is no clear Correcttrend over the past 30 years.

(d) Fit a moving average (e.g., period 2) to the data. Is it useful?

Yes Correct, a two year or three year moving average would be an acceptable Correctforecasting method for this time series.

(e) Make a forecast for 2016 using a method of your choice (including a judgment forecast). Justify your method. (Round your answer to the nearest whole number.)

The two year moving average forecast for 2016 is .
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Item 9
Number of Certified Organic Farms
in the United States, 2001–2008
Year Farms
2001 5,357
2002 5,736
2003 6,470
2004 6,456
2005 6,887
2006 7,820
2007 9,398
2008 10,778


Click here for the Excel Data File

(a) Fit three trends (linear, quadratic, exponential) to the time series. (A negative value should be indicated by a minus sign. Do not round the intermediate calculations. Round your final answers to 2 decimal places.)

Linear yt = xt +

Quadratic yt = xt2+ xt +

Exponential yt = e x


(b) Use each of the three fitted trend equations to make numerical forecasts for the next three years. (Round the intermediate calculations to 2 decimal places and round your final answers to 1 decimal place.)

t Linear Exponential Quadratic
9

10

11


10
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Item 10
Daily Spot Exchange Rate, U.S. Dollars per Pound Sterling
Date Rate Date Rate Date Rate
1-Apr-04 1.8664 13-Apr-04 1.8260 23-Apr-04 1.7674
2-Apr-04 1.8393 14-Apr-04 1.7702 26-Apr-04 1.7857
5-Apr-04 1.8140 15-Apr-04 1.7785 27-Apr-04 1.7925
6-Apr-04 1.8474 16-Apr-04 1.8104 28-Apr-04 1.7720
7-Apr-04 1.8410 19-Apr-04 1.8155 29-Apr-04 1.7856
8-Apr-04 1.8425 20-Apr-04 1.7816 30-Apr-04 1.7844
9-Apr-04 1.8322 21-Apr-04 1.7620 3-May-04 1.7720
12-Apr-04 1.8458 22-Apr-04 1.7684 4-May-04 1.7607
5-May-04 1.7732 13-May-04 1.7554 21-May-04 1.7880
6-May-04 1.7841 14-May-04 1.7692 24-May-04 1.7908
7-May-04 1.7942 17-May-04 1.7595 25-May-04 1.8535
10-May-04 1.7623 18-May-04 1.7795 26-May-04 1.8342
11-May-04 1.7644 19-May-04 1.7727 27-May-04 1.8769
12-May-04 1.7743 20-May-04 1.7610 28-May-04 1.8730


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(a) Make a line chart and fit an m-period moving average to the exchange rate data shown above with m = 2, 3, 4, and 5 periods. For each method, state the last MA value. (Round your answers to 4 decimal places.)

m-period Next period forecast
2
3
4
5


(b) Which value of m do you prefer?

The preferred m value is 5 Correct. This value of m Using this allows one to forecast the weekly or monthly trends without Correctoverreacting to daily fluctuations.

(c) Is a moving average appropriate for this kind of data?

Yes Correct, a moving average is appropriate Correct.
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Item 11
Daily Spot Exchange Rate, U.S. Dollars per Pound Sterling
Date Rate Date Rate Date Rate
1-Apr-04 1.8564 13-Apr-04 1.8160 23-Apr-04 1.7674
2-Apr-04 1.8293 14-Apr-04 1.7902 26-Apr-04 1.7857
5-Apr-04 1.8140 15-Apr-04 1.7785 27-Apr-04 1.7925
6-Apr-04 1.8374 16-Apr-04 1.8004 28-Apr-04 1.7720
7-Apr-04 1.8410 19-Apr-04 1.8055 29-Apr-04 1.7751
8-Apr-04 1.8325 20-Apr-04 1.7914 30-Apr-04 1.7744
9-Apr-04 1.8322 21-Apr-04 1.7720 3-May-04 1.7720
12-Apr-04 1.8358 22-Apr-04 1.7684 4-May-04 1.7907
5-May-04 1.7932 13-May-04 1.7584 21-May-04 1.7880
6-May-04 1.7941 14-May-04 1.7572 24-May-04 1.7908
7-May-04 1.7842 17-May-04 1.7695 25-May-04 1.8135
10-May-04 1.7723 18-May-04 1.7695 26-May-04 1.8142
11-May-04 1.7544 19-May-04 1.7827 27-May-04 1.8369
12-May-04 1.7743 20-May-04 1.7710 28-May-04 1.8330


Click here for the Excel Data File

(a) Make a line chart and fit an m-period moving average to the exchange rate data shown above with m = 2, 3, 4, and 5 periods. For each method, state the last MA value. (Round your answers to 4 decimal places.)

m-period Next period forecast
2
3
4
5


(b) Which value of m do you prefer?

The preferred m value is 5 Correct. This value of m Using this allows one to forecast the weekly or monthly trends without Correctoverreacting to daily fluctuations.

(c) Is a moving average appropriate for this kind of data?

Yes Correct, a moving average is appropriate Correct.
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Item 12
Daily Spot Exchange Rate, U.S. Dollars per Pound Sterling
Date Rate Date Rate Date Rate
1-Apr-04 1.8664 13-Apr-04 1.8260 23-Apr-04 1.7674
2-Apr-04 1.8393 14-Apr-04 1.7902 26-Apr-04 1.7857
5-Apr-04 1.8140 15-Apr-04 1.7785 27-Apr-04 1.7925
6-Apr-04 1.8474 16-Apr-04 1.8104 28-Apr-04 1.7720
7-Apr-04 1.8410 19-Apr-04 1.8055 29-Apr-04 1.7851
8-Apr-04 1.8425 20-Apr-04 1.7914 30-Apr-04 1.7844
9-Apr-04 1.8422 21-Apr-04 1.7720 3-May-04 1.7720
12-Apr-04 1.8458 22-Apr-04 1.7684 4-May-04 1.7607
5-May-04 1.7832 13-May-04 1.7584 21-May-04 1.7880
6-May-04 1.7841 14-May-04 1.7572 24-May-04 1.7908
7-May-04 1.7942 17-May-04 1.7595 25-May-04 1.8335
10-May-04 1.7823 18-May-04 1.7795 26-May-04 1.8342
11-May-04 1.7644 19-May-04 1.7827 27-May-04 1.8769
12-May-04 1.7743 20-May-04 1.7710 28-May-04 1.8530


Click here for the Excel Data File

(b) Perform simple exponential smoothing (using Excel’s Data Analysis or other software such as Minitab) using α = .05, .10, .20, and .50.

The degree of smoothing dramatically as α is increased.

(c) Which value of α do you prefer? (Round your answers to 2 decimal places.)

α =

(d) Is exponential smoothing appropriate for this kind of data?

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Item 14
U.S. Manufactured General Aviation Shipments, 2009–2015
Year Qtr 1 Qtr 2 Qtr 3 Qtr 4 Total
2009 395 408 387 485 1,675
2010 238 376 282 430 1,326
2011 270 283 273 475 1,301
2012 308 349 333 513 1,503
2013 325 409 347 507 1,588
2014 342 364 365 522 1,593
2015 285 379 387 554 1,605


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(a) Plot the data on airplane shipments.

Plot A Plot B Plot C Plot D

multiple choice 1
• Plot A Correct
• Plot B
• Plot C
• Plot D

(b) Can you see seasonal patterns?

multiple choice 2
• Yes Correct
• No

(c) Use MegaStat or Minitab to calculate estimated seasonal indexes and trend. (Round your answers to 3 decimal places.)

Qtr1
Index Qtr2
Index Qtr3
Index Qtr4
Index


(d) In which quarters are shipments highest? Lowest? (You may select more than one answer. Click the box with a check mark for the correct answer and double click to empty the box for the wrong answer.)

Highest

check all that apply 1
• Q1unanswered
• Q2unanswered
• Q3unanswered
• Q4unanswered

Lowest

check all that apply 2
• Q1unanswered
• Q2unanswered
• Q3unanswered
• Q4unanswered

(e) Is there a trend in the deseasonalized data?

There is a slight trend in the deseasonalized data.

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Item 15
Coca-Cola Revenues ($ millions), 2005–2010
Quarter 2005 2006 2007 2008 2009 2010
Qtr1 5,206 5,226 6,103 7,379 7,169 7,525
Qtr2 6,310 6,476 7,733 9,046 8,267 8,674
Qtr3 6,037 6,454 7,690 8,393 8,044 8,426
Qtr4 5,551 5,932 7,331 7,126 7,510 10,494


Click here for the Excel Data File

(a-1) Use MegaStat or Minitab to deseasonalize Coca-Cola’s quarterly data. (Round your answers to 3 decimal places.)

 1   2   3   4

2005

2006

2007

2008

2009

2010

mean


(a-2) State the adjusted four quarterly indexes. (Round your answers to 3 decimal places.)

Q1 Q2 Q3 Q4


(a-3) What is the trend model for the deseasonalized time series? (Round your answers to 2 decimal places.)

yt = xt +

(b) State the model found when performing a regression using seasonal binaries. (A negative value should be indicated by a minus sign. Round your answers to 4 decimal places.)

yt = + t + Q1 + Q2 + Q3

(c) Use the regression equation to make a prediction for each quarter in 2011. (Enter your answers in millions rounded to 3 decimal places.)

Quarter Predicted
Q1

Q2

Q3

Q4

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