This paper circulates around the core theme of 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 together with its essential aspects. It has been reviewed and purchased by the majority of students thus, this paper is rated 4.8 out of 5 points by the students. In addition to this, the price of this paper commences from £ 99. To get this paper written from the scratch, order this assignment now. 100% confidential, 100% plagiarism-free.
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
|
0.0 |
