Create scatter plot and insert regression line and equation

Reference no: EM13389560

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

Reference no: EM13389560

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