1. If the price for some good increases by 10% and the quantity demanded falls by 5%, (a) what is the price elasticity of demand, and (b) is this elastic or inelastic?
2. Last year the US lowcostcarrier Spirit Airlines entered the DallasChicago market. The average ticket price for all airlines servicing the route fell from $200 to $180. After Spirit's entry, the number of passengers increased from 700 to 800 per day (these number are hypothetical, but reasonable). Calculate the price elasticity of demand between these two points. Show the computation.
3. An airline consulting firm as determined that the income elasticity for leisure air travel in China is 1.5. If incomes increase by 5% next year, what is the percentage change in leisure passengers expected next year? Show the computation.
4. The state operates a toll road which currently charges $1.00 per car with 100,000 cars using the road daily. The state wishes to raise an additional $10,000 per day for road maintenance. A newly hired financial analyst proposes raising the toll to $1.10 per car. The analyst reports to you. Will you accept and forward her recommendation to your boss?
5. The demand curve for a product is given by Q^{d}_{x} = 1,000  2P_{x} +.02P_{z }where P_{z} = $400. (Hint: If you're not comfortable with the calculus alternatives, compute Q at the given prices, then again with a 1% increase in price. Then figure percentage change in Q over the percentage change in P, %?Q/%?P).
 What is the own price elasticity of demand when P_{x} = $154? Is the demand elastic or inelastic? What would happen to the firm's revenue if it decided to charge a price below $154?
 What is the own price elasticity of demand when P_{x} = $354? Is the demand elastic or inelastic? What would happen to the firm's revenue if it decided to charge a price below $354?
 What is the crossprice elasticity of demand between good X and good Z when P_{x} = $154? Are good X and good Z substitutes are complements?
6. 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

9.342E08


Residual

15

6951.532

463.4355




Total

16

49066.22






Coefficients

Standard Error

t Stat

Pvalue

Lower 95%

Upper 95%

Intercept

275.7148

25.82438

10.67653

2.1E08

220.6713974

330.7581059

GDP

0.019662

0.002063

9.532837

9.34E08

0.015265596

0.02405796
