Analysis of Cost Behavior for JetBlue Airways

JetBlue Airways is a major American low-cost airline and the seventh-largest airline in the United States by

passengers carried and have headquarters in Long Island, New York. They have a fleet of 262 aircraft and offer

flight amenities from their "Core" experience which includes unlimited brand-name snacks and soft drinks,

to their "Mint" experience, which includes tapas-style dining, lie-flat seating, complimentary pre-departure

drink with premium spirits, etc.

JetBlue Airways
			Revenue
	Operating	Operating	passengers
	revenues	expenses	carried	Departures
Year	(thousands)	(thousands)	(thousands)	(thousands)
2019	$8,094,000	$7,294,000	42,728	368
2018	7,658,000	7,392,000	42,150	366
2017	7,012,000	6,039,000	40,038	353
2016	6,584,000	5,324,000	38,263	337
2015	6,416,000	5,200,000	35,101	316
2014	5,817,000	5,302,000	32,078	294
2013	5,441,000	5,013,000	30,463	282
2012	4,982,000	4,606,000	28,956	264
2011	4,504,000	4,182,000	26,370	243
2010	3,779,000	3,446,000	24,254	225

1. Prepare a simple regression of Operating Expenses as a function of Revenue passengers carried.

Prepare a plot of the regression results. Include in your XY scattergram a trendline, R², and regression

formula. Be sure the regression formula is not in scientific notation. Please note that JetBlue shows

Operating expenses in $millions and revenue passengers in thousands. I suggest, for ease of interpretation,

that you convert one of the two amounts such that they are stated in the same denomination.

For example, JetBlue reports 2019 Operating expenses as $7,294 million. I converted operating expenses

for my analysis to thousands, with the amount after conversion being $7,294,000 thousand.

Be sure your regression line exactly intersects the axis (either Y or X, whichever it may be).

2. Prepare a simple regression of Operating revenues as a function of Revenue passengers carried.

Prepare a plot of the regression results. Include in your XY scattergram a trendline, R², and regression

formula. Be sure the regression formula is not in scientific notation. Please note that Operating

revenues is shown in $millions. I suggest, for ease of interpretation, making the same adjustment to

Revenue passengers as I recommend in #1.

Be sure your regression line exactly intersects the axis (either Y or X, whichever it may be).

3. Prepare a simple regression of Operating expenses as a function of an average number of departures.

Prepare a plot of the regression results. Include in your XY scattergram a trendline, R², and

regression formula. Be sure the regression formula is not in scientific notation. Again please note

that Departures are the actual number, they are not summarized in thousands or millions.

4. In other words, I recommend restating Departures into thousands, for example, I restated 2019 Departures

of 368,355 to 369.

5. Using the regression equations derived in #1 and #2, estimate the number of Revenue passengers to

be carried for JetBlue to break even. Subtract the Operating expense equation from the

Operating revenues equation, plugging in Revenue passengers carried until the result is $0. That will

be your break-even point, on average, over the years 2010 - 2019?

6. I am sure you noticed the negative intercepts in two of your regression equations? This can be explained

through a concept referred to as the "relevant range" in a regression formula. Would you please explain the

regression intercepts using the "relevant range" definition?

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