Convert the data into logarithms and run

Reference no: EM131787500

There is quarterly information from 2010 to 2013 about the FlyingHigh (FH) airline.

This data is reported in the following table:

Year	Quarter	QD	PFH	PR	M
2010	Q1	64.8	250	250	104
	Q2	33.6	265	250	101.5
	Q3	37.8	265	240	103
	Q4	83.3	240	240	105
2011	Q1	111.7	230	240	100
	Q2	137.5	225	260	96.5
	Q3	109.6	225	250	93.3
	Q4	96.8	220	240	95
2012	Q1	59.5	230	240	97
	Q2	83.2	235	250	99
	Q3	90.5	245	250	102.5
	Q4	105.5	240	240	105
2013	Q1	75.7	250	220	108.5
	Q2	91.6	240	230	108.5
	Q3	112.7	240	250	108
	Q4	102.2	235	240	109

Where: QD is the average number of seats in economy class in FH; PFH is the average price (in US dollars) of the seat in economy class of FH; PR is the average price (in US Dollars) of the seat in the economic class of the competitor closest; and, M is the income level (in thousands of American dollars) of the region where FH operates and its main competitor.

According to this:

1- Show the correlations between QD, PFH, PR, and, M. Relief the signs of the correlations that has QD with the rest of variables.

According to economic theory have, Did you sense these signs?

correlations	QD	PFH	PR	M
QD	1	-0.76558	0.227258	-0.11685
PFH		1	-0.15292	0.506505
PR			1	-0.49197
M				1

Now the correlation of PFH is negative with QD because more the seats , less will be the price, negative of PR and PFH , because if the PFH has high price competitor will keep it low so opposite correlation. M is positive with PFH , because if income is high he will be ready to pay high for seats.

Convert the data into logarithms and run the following specification:

Ln(QD)_t = a + b * Ln(PFH)_t + c * Ln(PR)_t + d * Ln(M)_t + error_t

Expected signs b <0 by the demand law and c> 0. a is the intercept, and, t: 1,2,3, ...., 16.

2-With Minimum Ordinary Squares (MCO), using Excel (Data Analysis Regression), run the regression of the previous specification and show the estimates of a, b, c, y,d. Present a summary of your "outcome"

2- Indicate if the estimates have the expected signs. Use the definitions of elasticities
Negative sign with log PFH is nice because more seats means less price, log PR is positive because if the competitor is increasing the price , then in order to lower the price one has to increase seats , income is positive because if people are ready to pay more , then more benefit from high no of seats.

3- Indicate if the estimates are statistically significant at 0.1%, 1%, 5% or 10%.

4- df. 1.345 1.761 2.145 2.624 2.977 3.787

5- ν 0.90 0.95 0.975 0.99 0.995 0.999

so when we see the log PFH , it Is not significant at any level, log PR is significant at 10 & 5% , log M is significant at all level.
6- Interpret the coefficient of determination (R-squared) and the test F.

R square is high because the model is good, F test is significant because all the variables are not zero.

7- How does QD change, if PFH goes up by 10%? Is FH an inelastic good? Explain

Is it a good decision to raise the price?

The PFH variable is not significant, so we won't see its impact on QD change

7- How does QD change, if PR drops by 11%?
If PR drops by 11 % , the QD will drop by more than 22 %

8- How does QD change, if M goes up by 5%?
If M goes up by 5 % QD goes up by more than 23 %

Reference no: EM131787500

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