Question

Please answer the following questions based on the given graph

YEAR	Year Number	Domestic
1997	1	3210113
1998	2	3294244
1999	3	3150826
2000	4	3244421
2001	5	3358399
2002	6	3289148
2003	7	3326111
2004	8	3423024
2005	9	3772952
2006	10	4349081
2007	11	4937099
2008	12	5106860
2009	13	4704189

(1) Create a Time Series (Trend)Model  for  passengers on Domestic flights. (To zero decimal places) The predicted amount of passengers for 2010 on Domestic flights is ________.

(2) Create a Time Series (Trend)Model  for  passengers on Domestic flights. (To zero decimal places) On average, the number of passengers of domestic flights increase by ________each year, keeping all else equal.

(3)Create a GrowthModel  for  passengers on Domestic flights. (To zero decimal places) The predicted amount of passengers for 2010 on Domestic flights is ________.

(4)Create a Growth Model  for passengers on Domestic flights. (To two decimal places) On average, the number of passengers of domestic flights increase by ________percent each year, keeping all else equal.

(5) Based on R-squared which model is better for predicting passengers of domestic flights?
Time Series (Trend) Model
Growth Model

Answer 1

For part 1, the predicted amount of passengers for 2010 on Domestic flights is 4,914,712 passengers using the Trend function [=TREND(B2:B14,A2:A14,A15:A17)] in Excel 2007

For part 2, since it is a linear curve, the diffence in the trend will give increase in passengers per year, that is 161,811

For part 3, we use the Growth function [=GROWTH(C2:C14,B2:B14,B15)]

YEAR	Domestic
1997	3210113
1998	3294244
1999	3150826
2000	3244421
2001	3358399
2002	3289148
2003	3326111
2004	3423024
2005	3772952
2006	4349081
2007	4937099
2008	5106860
2009	4704189
2010	4955973

4. We calculate the percentage [=(C16-C15)*100/C15] after extrapolating using the GROWTH function.

2011	5162572	4.17%
2012	5377784	4.17%
2013	5601967	4.17%
2014	5835496	4.17%
2015	6078760	4.17%
2016	6332164	4.17%
2017	6596133	4.17%

5.

[=CORREL(B2:B22,C2:C22)] and then it is squared, or else [=RSQ(B2:B22,C2:C22)]

2010	4914712
2011	5076523
2012	5238334
2013	5400145
2014	5561956
2015	5723767
2016	5885578
2017	6047389
			0.964582
			0.930419	0.930419

Table for growth

2010	4955973
2011	5162572	4.17%
2012	5377784	4.17%
2013	5601967	4.17%
2014	5835496	4.17%
2015	6078760	4.17%
2016	6332164	4.17%
2017	6596133	4.17%
			0.968536
			0.938062	0.938062

Although both have data around the mean, the higher R2 value of the growth model indicates that it is a better fit as compared to the trend model.