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  • The data are real US Gross Domestic Product (in billions of dollars) and Domestic Revenue Passenger Miles (in millions) for the years 1996 through 2012. Below this table is the MS Excel Summary Output regressing RPMs against GDP. Using MS Excel or another similar application, build a scatter plot and insert the regression line and equation. Next, interpret the regression output and explain the regression statistics. Be certain that the regression coefficients match those in the scatter plot equation. Finally, use the regression equation to predict RPMs for 2013 and 2014 assuming GDP grows by 3% each year from 2012. You may wish to check the actual RPMs to see how closely your estimate matched. Note:? To build a scatter plot in Excel, select and copy the GDP and RPM data into Excel; select the data in Excel, then use Insert/Scatter to create a scatter plot. Finally, scroll down Chart Layout to select the format that creates a regression line and formula. Use the Excel Help function as needed.


Year

GDP

RPM

1996

8,100.2

419.07

1997

8,608.5

438.42

1998

9,089.1

448.58

1999

9,665.7

472.96

2000

10,289.7

500.12

2001

10,625.3

472.60

2002

10,980.2

469.96

2003

11,512.2

492.73

2004

12,277.0

542.82

2005

13,095.4

569.24

2006

13,857.9

574.52

2007

14,480.3

592.33

2008

14,720.3

568.25

2009

14,417.9

538.98

2010

14,958.3

552.85

2011

15,533.8

563.65

2012

16,244.6

568.70

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.926457

R Square

0.858323

Adjusted R Square

0.848878

Standard Error

21.52755

Observations

17

ANOVA

df

SS

MS

F

Significance F

Regression

1

42114.69

42114.69

90.87497

9.342E-08

Residual

15

6951.532

463.4355

Total

16

49066.22

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

275.7148

25.82438

10.67653

2.1E-08

220.6713974

330.7581059

GDP

0.019662

0.002063

9.532837

9.34E-08

0.015265596

0.02405796

 


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DATE ANSWERED

Apr 19, 2020

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