a case study on cost estimation and pro_tability analysis at continental airlines.pdf

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ISSUES IN ACCOUNTING EDUCATION American Accounting AssociationVol. 26, No. 1 DOI: 10.2308/iace.2011.26.1.1812011pp. 181–200

A Case Study on Cost Estimation andProfitability Analysis at Continental Airlines

Francisco J. Román

ABSTRACT: This case exposes students to the application of regression analyses tobe used as a tool pursuant to understanding cost behavior and forecasting future costsusing publicly available data from Continental Airlines. Specifically, the case focuses onthe harsh financial situation faced by Continental as a result of the recent financial crisisand the challenges it faces to remain profitable. It then highlights the importance ofreducing and controlling costs as a viable strategy to restore profitability and how re-gression analysis can assist in this pursuit. Students are next presented with quarterlydata for various categories of costs and several potential cost drivers, which they mustuse to perform regressions on operating costs using a variety of cost drivers. They mustthen use their regression results to forecast operating costs and conduct a profitabilityanalysis to project quarterly profits for the upcoming fiscal year. Finally, students mustsummarize the main results of their analysis in a memorandum addressed to Continen-tal’s management, providing recommendations to restore profits. In particular, the con-cept of mixed cost functions is reinforced, as is the understanding of the steps requiredto perform regression analysis in Excel, interpreting the regression output, and theunderlying standard assumptions in regression analysis. The case has been tested andwell received in an intermediate cost accounting course and it is suitable for bothundergraduate and graduate students.

Keywords: cost estimation; profitability analysis; cost behavior; regression analyses;cost functions.

Data Availability: All data are from public sources and are available in hard copy insidethe case. Data are also available in electronic form by the authorupon request.

INTRODUCTIONn 2008, the senior management team at Continental Airlines, commanded by Lawrence Kell-ner, the Chairman and Chief Executive Officer, convened a special meeting to discuss thefirm’s latest quarterly financial results. A bleak situation lay before them. Continental had

ncurred an operating loss of $71 million dollars—its second consecutive quarterly earnings de-

rancisco J. Román is an Assistant Professor at Texas Tech University.

thank Kent St. Pierre �editor�, Michael Costa, and two anonymous referees for their suggestions on previous versions ofhe case.

ditor’s note: Accepted by Kent St. Pierre

Published Online: February 2011

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line that year. Likewise, passenger volume was significantly down, dropping by nearly 5 percentrom the prior year’s quarter. Continental’s senior management needed to act swiftly to reversehis trend and return to profitability.

Being the fourth largest airline in the U.S. and eighth largest in the world, Continental waserceived as one of the most efficiently run companies in the airline industry. Nonetheless, 2008rought unprecedented challenges for Continental and the entire industry as the United States anduch of the world was heading into a severe economic recession. Companies cutting deeply into

heir budgets for business travel, the highest yielding component of Continental’s total revenue,ogether with a similar downward trend from the leisure and casual sector, combined to sharplyeduce total revenue.

Concurrent with this revenue decline, the price of jet fuel soared to record levels during008.1 Thus, while revenue was decreasing, Continental was paying almost twice as much in fuelosts. Interestingly, fuel costs surpassed the firm’s salaries and wages as the highest cost inontinental’s cost structure. This obviously had a negative impact on the bottom line, squeezingven further the already strained profit margins.

The outlook for a quick recovery in the U.S. economy and, consequently, an upturn in theemand for air travel in the short term did not seem likely. Continental’s internal forecasts indi-ated that a further decline in passenger volume should be anticipated throughout 2009, with aecovery in travel possibly occurring by the middle of 2010.

To summarize, adverse economic conditions in the U.S., coupled with the rise in fuel costs,ere dragging down Continental’s profits and relief was unlikely through the foreseeable future.

THE DECISION TO REDUCE FLYING CAPACITY AND THE IMPACT ONOPERATING COSTS

Given the situation described above, management needed to act swiftly to restore profitability.everal strategic options were evaluated. Since the U.S. and much of the world was facing aevere recession, the prospect for growing revenues by either raising airfares or passenger volumeeemed futile. Contrary to raising revenue, Continental’s managers believed that raising faresould potentially erode future revenues beyond the present level. Discounting fares did not seemplausible solution either, because given the severity of the economic situation a fare cut could

all short in stimulating additional passenger demand and lead to lowering revenues.Thus, because management anticipated that revenues would remain flat for most of the year,

he only viable short-term solution to restoring profits was a substantial and swift reduction inperating costs. This could most effectively be accomplished in two ways. First, through a reduc-ion in flying capacity adjusted to match projected passenger demand. With this in mind, Conti-ental’s management agreed to reduce flying capacity by 11 percent on domestic and internationaloutes.2 As a result of this action, Continental would eliminate the least profitable �or unprofitable�ights and, accordingly, would ground several planes in the fleet. Management anticipated that thisecision would reduce several of the firm’s operating costs.

Apart from this, Continental could achieve further reductions in costs by implementing sev-ral cost-cutting initiatives and through operational efficiencies. For example, management pro-

To illustrate, jet fuel is tied to the price of oil and, over the past year, oil prices surged from about $70 to $135 per barrel.Consequently, the price of jet fuel increased markedly, from an average of $1.77 per gallon to $4.20 by the mid-summerof 2008.Specifically, on June 13, 2008, Continental Airlines announced that it planned to reduce its flight capacity by 11 percent.By shrinking capacity, Continental expected to reduce the number of domestic and international flights from its threemajor hubs in Houston, Cleveland, and Newark �Maynard 2008�.

ssues in Accounting Education Volume 26, No. 1, 2011merican Accounting Association

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A Case Study on Cost Estimation and Profitability Analysis at Continental Airlines 183

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ected that it could achieve reductions in Passenger Services expenses by consolidating severalasks during passenger check-in and by reducing food and beverage waste served during flights.dditionally, the firm could reduce various miscellaneous expenses through targeted cuts in dis-

retionary spending.In sum, to close the gap in profitability, Continental’s strategy was geared toward slashing

perating costs by cutting capacity and through aggressive identification and implementation ofost-cutting initiatives.

The next step would be for management to know precisely how their decision to downsizeapacity would impact the firm’s future operating costs, and also identify specific areas in whichhe firm could achieve additional cost reductions. Additionally, the cost analysis would helporecast the firm’s operating costs and projected profits �or losses� for the upcoming fiscal year.owever, before we can proceed with such analysis, an examination of how the various categoriesf Continental’s costs behave is in order.

Before we begin, let us prepare with an overview of the airline industry and its competitiveandscape, and an understanding of why cost behavior bears particular relevance in this case.

Relative to other industries, airlines are a very difficult business to manage. In particular, theyre exposed to tremendous risks brought by volatility inherent in their business model, as they dealith high fixed costs, labor unions, instability in fuel prices, weather and natural disasters, pas-

enger safety, and security regulations. These aspects bring a large burden to airlines’ cost struc-ures. Moreover, competition within the industry is fierce; the proliferation of discount carriers,uch as Southwest Airlines and, most recently, Jet Blue, and the end of fare regulation in 1978, hasindered airlines’ pricing power and their ability to spur revenues. For these reasons, cost con-ainment is a critically important aspect of profitability in this industry.

In order for Continental to restore profitability in this harsh environment of weak demand forir travel, it must be able to contain its operating costs, especially its massive fixed costs, whichre visible in several ways. For example, salaries for pilots, flight attendants, and mechanics, asell as aircraft leasing costs, are typically fixed, varying little with shifts in passenger volume.ecause fixed costs typically embody the amount of operating capacity of a firm, they are com-only referred as “capacity” costs. Since fixed costs do not self-adjust to fluctuations in passenger

olume, the only way in which they can be decreased �or increased� is if management adjusts themn accordance to the level of operating capacity. In contrast, other costs, such as passenger servicesnd reservation and distribution costs, behave as variable and would self-adjust with variations inolume or operating activity.

Hence, to assess the impact of this strategic decision to alter Continental’s cost structure, anddentify the areas that could achieve the greatest reduction in costs, we must resolve how Conti-ental’s operating costs behave and what drives them. In what follows, we learn how to applyegression analyses to examine cost behavior and forecast future costs, and then use that knowl-dge to assess how the reduction in flying capacity would affect Continental’s operating costs androfitability in the near term.

ESTIMATING COSTS USING REGRESSION ANALYSESThe previous discussion highlighted the importance of examining the behavior of Continen-

al’s operating costs to pave the way for a cost and profitability analysis using regression analysis.egression analysis is a powerful statistical tool that is frequently used by firms to examine costehavior and predict future costs. The idea behind regression analysis is straightforward: historicalata for costs, and the various activities that could potentially drive operating costs, are insertednto a mathematical calculation which yields the average amount of change in that particular costhat has occurred over time. Average values provided by regression calculations may then bepplied to estimate future change that will occur in that cost given a one-unit change in one or

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ore of the business activities which drive that cost.3 More precisely, in a regression model, costs a function of one or more business activities �or factors� underlying a business operation.imply put, the business activities are the drivers of operating costs. Therefore, since activitiesrive costs, our first step in the estimation of a cost function is to identify the underlying activitiesr other potential factors that drive the cost in question—the cost drivers. This requires extensivenowledge of the business operation. In the case of Continental Airlines, the potential drivers ofperating costs vary greatly. For instance, as previously noted, the number of passengers thatontinental flies may drive the costs related to Passenger Services. Likewise, Aircraft Mainte-ance and Repairs costs could be driven by the number of aircraft in the fleet and by the level ofying capacity set by Continental �i.e., available seat miles�.

In synthesis, to predict how Continental’s operating costs would be affected by the decision toeduce capacity, and to identify those areas in which additional room is available for cost cutting,e need to identify which costs in this firm’s cost structure behave as variable, fixed, or mixed �inhich elements of both variable and fixed are observable�. Equally important, we should also

dentify the specific drivers �if any� of each cost.Your job is to assist management in their quest to restore profitability at Continental Airlines.

pecifically, you must conduct regression analyses to examine cost behavior and then use thisnformation to forecast operating costs and profitability for the upcoming year. As part of your costnalysis, you should investigate how the decision to cut flying capacity would impact the firm’suture operating costs and, equally important, identify those specific expense categories �or oper-ting areas� in which this firm could attain additional costs saving by implementing cost-cuttingnitiatives. Your conclusions should be outlined in a memorandum directed to Continental’s Ex-cutive management team.

You are provided next with a description of Continental’s operating costs and the potentialrivers of costs so you can conduct regression analysis to estimate the corresponding cost func-ions. To help you in estimating the regressions, a comprehensive set of instructions for perform-ng regression analysis using Microsoft Excel is provided in the Appendix. Immediately followinghe description of costs, a series of questions is provided that should help guide your analysis.dditionally, to help you estimate your regressions, Exhibit 1 presents past quarterly data for all of

he above expenditures for the period of January 2000 through December 2008, while Exhibit 2rovides quarterly operations data for the same period of time.

CONTINENTAL’S OPERATING COSTS AND POTENTIAL COST DRIVERSAs shown in Exhibit 1, there are ten categories of operating costs. These include salaries and

ages, aircraft fuel and related taxes, aircraft rentals, airport fees, aircraft maintenance andepairs, depreciation and amortization, distribution costs, passenger services, regional capacityurchases, and other expenses. Of these, some represent a single expense item. For example, theost of aircraft rentals and airport fees together comprise a single cost item. Other costs representost pools comprising several cost items. Such is the case of passenger services and other ex-enses. The following provides a detailed description of each cost, along with the potential costrivers.4

For ease in exposition, cost functions and regression analyses are discussed briefly here. For further insight on costfunctions and on the mechanics of regression analyses, I refer the reader to the Appendix.A cost driver represents a particular business activity, which usually tends to have a cause-and-effect relationship witha given cost. For example, for airlines, a typical cost driver for landing fees is the number of daily flights carried by theairline, as well as the number of passengers flown. An increase �decrease� in the number of flights or passengers flownwould increase �decrease� landing fees.


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EXHIBIT 1

REVENUES AND OPERATING COSTS DATA

bs. Period Revenues FuelSalaries and

WagesCapacityPurchases

AircraftRentals Landing Fees

1Q-2000 2,277,000,000 334,000,000 672,000,000 — 206,000,000 129,000,0002Q-2000 2,571,000,000 313,000,000 719,000,000 — 210,000,000 138,000,0003Q-2000 2,622,000,000 354,000,000 748,000,000 — 215,000,000 133,000,0004Q-2000 2,429,000,000 392,000,000 736,000,000 — 213,000,000 132,000,0001Q-2001 2,451,000,000 345,000,000 758,000,000 — 214,000,000 141,000,0002Q-2001 2,556,000,000 349,000,000 800,000,000 — 223,000,000 153,000,0003Q-2001 2,223,000,000 322,000,000 779,000,000 — 230,000,000 139,000,0004Q-2001 1,739,000,000 213,000,000 684,000,000 — 236,000,000 148,000,0001Q-2002 1,993,000,000 208,000,000 732,000,000 — 228,000,000 161,000,000

0 2Q-2002 2,192,000,000 254,000,000 746,000,000 — 231,000,000 160,000,0001 3Q-2002 2,178,000,000 276,000,000 743,000,000 — 227,000,000 163,000,0002 4Q-2002 2,039,000,000 285,000,000 738,000,000 — 216,000,000 149,000,0003 1Q-2003 2,042,000,000 347,000,000 778,000,000 — 223,000,000 152,000,0004 2Q-2003 2,216,000,000 302,000,000 762,000,000 — 224,000,000 152,000,0005 3Q-2003 2,365,000,000 316,000,000 778,000,000 — 225,000,000 165,000,0006 4Q-2003 2,247,000,000 290,000,000 738,000,000 158,000,000 224,000,000 151,000,0007 1Q-2004 2,307,000,000 333,000,000 688,000,000 317,000,000 220,000,000 160,000,0008 2Q-2004 2,553,000,000 387,000,000 711,000,000 328,000,000 222,000,000 163,000,0009 3Q-2004 2,602,000,000 414,000,000 703,000,000 347,000,000 224,000,000 171,000,0000 4Q-2004 2,437,000,000 453,000,000 717,000,000 359,000,000 225,000,000 160,000,0001 1Q-2005 2,505,000,000 470,000,000 715,000,000 353,000,000 227,000,000 171,000,0002 2Q-2005 2,857,000,000 575,000,000 649,000,000 382,000,000 229,000,000 181,000,0003 3Q-2005 3,001,000,000 684,000,000 646,000,000 406,000,000 234,000,000 182,000,0004 4Q-2005 2,845,000,000 714,000,000 639,000,000 431,000,000 238,000,000 174,000,0005 1Q-2006 2,947,000,000 672,000,000 661,000,000 415,000,000 245,000,000 185,000,0006 2Q-2006 3,507,000,000 744,000,000 791,000,000 454,000,000 248,000,000 198,000,0007 3Q-2006 3,518,000,000 858,000,000 743,000,000 475,000,000 249,000,000 195,000,0008 4Q-2006 3,156,000,000 760,000,000 680,000,000 447,000,000 248,000,000 186,000,0009 1Q-2007 3,179,000,000 684,000,000 726,000,000 430,000,000 248,000,000 193,000,0000 2Q-2007 3,710,000,000 842,000,000 821,000,000 444,000,000 248,000,000 190,000,0001 3Q-2007 3,820,000,000 895,000,000 836,000,000 446,000,000 249,000,000 209,000,0002 4Q-2007 3,523,000,000 933,000,000 744,000,000 473,000,000 249,000,000 198,000,0003 1Q-2008 3,570,000,000 1,048,000,000 729,000,000 506,000,000 247,000,000 207,000,0004 2Q-2008 4,044,000,000 1,363,000,000 704,000,000 589,000,000 246,000,000 210,000,0005 3Q-2008 4,072,000,000 1,501,000,000 765,000,000 553,000,000 244,000,000 225,000,0006 4Q-2008 3,471,000,000 993,000,000 760,000,000 425,000,000 240,000,000 210,000,000

bs. Period Distribution CostsAircraft

Maintenance DepreciationPassengerServices Other Expenses

1Q-2000 248,000,000 159,000,000 95,000,000 85,000,000 286,000,0002Q-2000 261,000,000 171,000,000 98,000,000 91,000,000 284,000,0003Q-2000 255,000,000 167,000,000 102,000,000 97,000,000 288,000,0004Q-2000 217,000,000 149,000,000 107,000,000 89,000,000 277,000,0001Q-2001 243,000,000 160,000,000 105,000,000 91,000,000 318,000,0002Q-2001 230,000,000 162,000,000 111,000,000 96,000,000 295,000,000

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bs. Period Distribution CostsAircraft

Maintenance DepreciationPassengerServices Other Expenses

3Q-2001 194,000,000 142,000,000 120,000,000 89,000,000 121,000,0004Q-2001 142,000,000 104,000,000 131,000,000 71,000,000 166,000,0001Q-2002 172,000,000 114,000,000 106,000,000 77,000,000 382,000,000

0 2Q-2002 158,000,000 119,000,000 112,000,000 73,000,000 454,000,0001 3Q-2002 138,000,000 119,000,000 112,000,000 78,000,000 276,000,0002 4Q-2002 124,000,000 124,000,000 114,000,000 68,000,000 277,000,0003 1Q-2003 127,000,000 133,000,000 116,000,000 70,000,000 320,000,0004 2Q-2003 138,000,000 126,000,000 110,000,000 73,000,000 91,000,0005 3Q-2003 131,000,000 135,000,000 110,000,000 81,000,000 250,000,0006 4Q-2003 135,000,000 115,000,000 108,000,000 73,000,000 455,000,0007 1Q-2004 137,000,000 112,000,000 104,000,000 69,000,000 304,000,0008 2Q-2004 140,000,000 102,000,000 105,000,000 76,000,000 279,000,0009 3Q-2004 139,000,000 107,000,000 104,000,000 84,000,000 287,000,0000 4Q-2004 136,000,000 93,000,000 102,000,000 77,000,000 278,000,0001 1Q-2005 138,000,000 112,000,000 99,000,000 77,000,000 316,000,0002 2Q-2005 154,000,000 106,000,000 98,000,000 84,000,000 280,000,0003 3Q-2005 154,000,000 116,000,000 97,000,000 91,000,000 282,000,0004 4Q-2005 142,000,000 121,000,000 95,000,000 80,000,000 305,000,0005 1Q-2006 160,000,000 127,000,000 96,000,000 82,000,000 293,000,0006 2Q-2006 178,000,000 140,000,000 97,000,000 90,000,000 323,000,0007 3Q-2006 157,000,000 140,000,000 99,000,000 97,000,000 313,000,0008 4Q-2006 155,000,000 140,000,000 99,000,000 87,000,000 333,000,0009 1Q-2007 161,000,000 144,000,000 99,000,000 90,000,000 340,000,0000 2Q-2007 176,000,000 169,000,000 101,000,000 99,000,000 357,000,0001 3Q-2007 171,000,000 166,000,000 106,000,000 105,000,000 357,000,0002 4Q-2007 174,000,000 142,000,000 107,000,000 95,000,000 328,000,0003 1Q-2008 182,000,000 159,000,000 106,000,000 96,000,000 356,000,0004 2Q-2008 194,000,000 167,000,000 108,000,000 107,000,000 427,000,0005 3Q-2008 182,000,000 152,000,000 112,000,000 113,000,000 461,000,0006 4Q-2008 159,000,000 135,000,000 111,000,000 91,000,000 372,000,000

OPERATIONS AND COST DRIVER DATA

bs. PeriodTotal

AircraftLeasedAircraft Flights Passengers Available Seat Miles

Available SeatMiles Regional

1Q-2000 514 403 98,820 11,201,000 20,951,000,000 —2Q-2000 522 410 97,871 12,084,000 21,384,000,000 —3Q-2000 535 414 97,967 12,155,000 22,356,000,000 —4Q-2000 522 398 98,378 11,456,000 21,409,000,000 —1Q-2001 548 406 98,590 11,220,000 21,459,000,000 —2Q-2001 557 416 99,018 12,256,000 22,813,000,000 —3Q-2001 501 377 98,564 11,254,000 21,994,000,000 —4Q-2001 522 393 81,109 9,508,000 18,219,000,000 —1Q-2002 538 400 81,883 12,062,000 20,375,000,000 —

0 2Q-2002 570 404 82,815 13,099,000 22,286,000,000 —1 3Q-2002 570 401 81,737 13,006,000 22,626,000,000 —2 4Q-2002 554 410 78,809 12,874,000 21,054,000,000 —3 1Q-2003 562 419 75,178 11,518,000 20,843,000,000 1,767,000,0004 2Q-2003 570 428 75,617 13,044,000 21,241,000,000 2,073,000,000



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OPERATIONS AND COST DRIVER DATA

bs. PeriodTotal

AircraftLeasedAircraft Flights Passengers Available Seat Miles

Available SeatMiles Regional

5 3Q-2003 570 428 76,297 13,727,000 22,819,000,000 1,605,000,0006 4Q-2003 579 434 75,650 13,769,000 21,907,000,000 2,980,000,0007 1Q-2004 586 437 74,859 12,810,000 22,670,000,000 2,400,000,0008 2Q-2004 587 440 75,816 14,558,000 24,150,000,000 2,603,000,0009 3Q-2004 592 445 74,211 14,862,000 24,674,000,000 1,999,000,0000 4Q-2004 594 448 74,443 14,252,000 23,588,000,000 3,408,000,0001 1Q-2005 598 453 71,494 14,122,000 23,585,000,000 2,740,000,0002 2Q-2005 604 459 74,651 15,540,000 25,482,000,000 3,026,000,0003 3Q-2005 611 466 74,630 15,905,000 26,833,000,000 3,112,000,0004 4Q-2005 622 477 75,886 15,448,000 25,720,000,000 3,095,000,0005 1Q-2006 630 483 74,962 15,594,000 26,117,000,000 3,082,000,0006 2Q-2006 634 484 77,729 17,596,000 28,259,000,000 3,374,000,0007 3Q-2006 648 482 77,468 17,328,000 29,262,000,000 3,503,000,0008 4Q-2006 648 480 79,030 16,601,000 27,280,000,000 3,292,000,0009 1Q-2007 630 446 78,601 16,176,000 27,250,000,000 3,126,000,0000 2Q-2007 625 418 82,582 18,120,000 29,592,000,000 3,177,000,0001 3Q-2007 631 415 81,118 17,901,000 30,346,000,000 3,193,000,0002 4Q-2007 628 415 80,850 16,733,000 28,550,000,000 3,104,000,0003 1Q-2008 641 414 76,719 16,440,000 28,376,000,000 3,098,000,0004 2Q-2008 630 390 76,096 17,108,000 30,304,000,000 3,450,000,0005 3Q-2008 653 412 78,599 17,962,000 30,383,000,000 3,390,000,0006 4Q-2008 632 397 76,000 15,183,000 26,448,000,000 3,046,000,000

bs. PeriodPassenger Miles

Flown Employees Fuel Price Fuel Consumed

1Q-2000 15,005,000,000 45,000 $0.829 377,000,0002Q-2000 16,491,000,000 45,500 $0.797 386,000,0003Q-2000 17,325,000,000 46,000 $0.865 398,000,0004Q-2000 15,340,000,000 45,944 $0.885 372,000,0001Q-2001 15,114,000,000 38,396 $0.856 369,000,0002Q-2001 17,053,000,000 39,000 $0.815 391,000,0003Q-2001 16,206,000,000 39,500 $0.824 373,000,0004Q-2001 12,767,000,000 39,461 $0.826 369,000,0001Q-2002 14,867,000,000 40,229 $0.644 308,000,000

0 2Q-2002 16,489,000,000 41,011 $0.723 332,000,0001 3Q-2002 16,960,000,000 41,809 $0.760 340,000,0002 4Q-2002 17,252,000,000 40,244 $0.740 316,000,0003 1Q-2003 14,352,000,000 38,960 $1.029 305,000,0004 2Q-2003 16,129,000,000 39,000 $0.881 308,000,0005 3Q-2003 18,041,000,000 39,500 $0.857 330,000,0006 4Q-2003 16,412,000,000 39,000 $0.872 314,000,0007 1Q-2004 16,255,000,000 38,240 $1.041 320,000,0008 2Q-2004 18,735,000,000 37,496 $1.787 347,000,0009 3Q-2004 19,922,000,000 36,766 $1.199 345,000,0000 4Q-2004 18,239,000,000 38,255 $1.190 321,000,0001 1Q-2005 18,112,000,000 41,831 $1.453 324,000,0002 2Q-2005 20,292,000,000 45,742 $1.670 344,000,000



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alaries and WagesThis account represents costs related to salaries and wages, as well as fringe benefits, of

ontinental’s workers. These include salaries for pilots and wages for flight attendants and groundrew, as well as wages for Continental’s mechanics. Additionally, a significant portion of thisalary pool represents wages of reservation specialists, customer service representatives at air-orts, and the salaries for administrative and support personnel �e.g., flight schedulers, technology

bs. PeriodPassenger Miles

Flown Employees Fuel Price Fuel Consumed

3 3Q-2005 21,762,000,000 50,018 $1.880 364,000,0004 4Q-2005 20,033,000,000 42,200 $1.776 344,000,0005 1Q-2006 20,336,000,000 42,600 $1.904 347,000,0006 2Q-2006 23,367,000,000 43,450 $2.110 375,000,0007 3Q-2006 24,042,000,000 41,500 $2.215 387,000,0008 4Q-2006 21,772,000,000 38,033 $2.064 362,000,0009 1Q-2007 21,450,000,000 41,800 $1.895 361,000,0000 2Q-2007 24,623,000,000 43,300 $2.079 395,000,0001 3Q-2007 25,422,000,000 41,400 $2.206 406,000,0002 4Q-2007 22,670,000,000 39,640 $2.499 380,000,0003 1Q-2008 22,280,000,000 43,000 $2.797 375,000,0004 2Q-2008 24,836,000,000 40,100 $3.856 389,000,0005 3Q-2008 24,746,000,000 43,500 $3.450 395,000,0006 4Q-2008 20,825,000,000 42,490 $2.925 339,000,000

EXHIBIT 2

PROJECTIONS OF REVENUES AND OPERATING ACTIVITY FOR YEAR 2009

ariable Quarter 1 Quarter 2 Quarter 3 Quarter 4

evenues $2,962,000,000 $2,767,000,000 $2,947,000,000 $2,462,000,000vailable seat miles 26,323,000,000 28,007,000,000 28,933,000,000 26,291,000,000vailable regional seat miles 2,971,000,000 3,044,000,000 3,130,000,000 3,002,000,000umber of passengers 14,408,000 16,348,000 16,795,000 15,258,000umber of planes 634 617 604 601umber leased planes 398 394 380 379rice of fuel �per gallon� $1.82 $2.07 $1.99 $1.98allons of fuel consumed 403,000,000 430,000,000 369,000,000 479,000,000

ll financial and operational data represent quarterly data for the quarter beginning January 2000 �Observation 1� throughecember 2008. Data have been compiled from Continental’s 8-K and10-K reports, submitted to the Securities andxchange Commission.

Definitions of Operations Variables:Available seat miles � the number of seats available multiplied by the number of miles flown;

Available regional seat miles � available seat miles on regional routes;Number of passengers � number of paying passengers flown;

Number of planes � number of planes in the fleet, including regional routes aircraft;Number of leased planes � number of leased planes;

Price of jet fuel � average price per gallon of jet fuel in the respective quarter; andGallons of fuel consumed � number of gallons of fuel consumed in the respective quarter.


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ersonnel, accountants, and division managers�. A possible cost driver of salaries is the availableeat miles.5

ircraft Fuel and Related TaxesThis represents the cost of jet fuel and related fuel taxes. Jet fuel cost tends to be driven by the

urrent price of jet fuel and gallons of jet fuel consumed.

ircraft RentalsThese are expenses for capital leases of aircraft. The main driver is the number of leased

lanes in Continental’s fleet, including regional jets operated on behalf of Continental by fouregional airlines under various capacity purchase agreements.

irport FeesRepresents landing fees and passenger security fees paid to the various domestic and inter-

ational airports where Continental flies. Landing fees are driven by the number of passengers.

ircraft Maintenance and RepairsThese are expenses associated with the service and maintenance of planes. These include

xpenses related to scheduled maintenance, spare parts and materials, and airframe and engineverhauls. The main drivers of these costs are the number of planes in the fleet and the number ofiles flown.

epreciation and AmortizationThis represents depreciation and amortization expenses of aircraft, ground equipment, build-

ngs, and other property. It must be emphasized that the largest portion of depreciation expenseelates to the depreciation of aircraft. Although depreciation expenses are driven by the acquisitionost of Continental’s capital assets, depreciation is greatly influenced by both company policy andccounting principles, such as the depreciation method, that a firm adopts.

istribution CostsThese expenses represent credit card discount fees, booking fees, and travel agency commis-

ions, all of which are affected by passenger revenue. Therefore, the driver of these costs is totalevenue.

assenger ServicesThis is also a cost pool that includes expenses related to processing and servicing passengers

rior to take-off, during flight, and after arrival at their destination. A significant portion of theseosts is generated by Continental’s Field Services Division, the main function of which is torovide service to planes prior to take-off. Some of these expenses relate to checking in passen-ers, handling luggage on and off planes, cleaning planes, stocking planes with beverage and food,nd refueling the aircraft prior to take-off. The potential cost driver of these costs is the number ofassengers.

egional Capacity PurchasesThese are costs related to the purchase of regional routes served by several regional airlines

n behalf of Continental �ExpressJet, Chautauqua, CommutAir, and Cogan�. These costs are

Available seat miles is calculated as the number of seats available for passengers multiplied by the number of scheduledmiles those seats are flown.


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riven by the combined flying capacity of the four airlines: available regional seat miles.

ther Expenses

This is a cost pool that comprises many ancillary and discretionary expenditures, includingechnology expenses, security and outside services, general supplies, and advertising and promo-ional expenses. Further, this cost pool contains various special charges for gains and losses fromhe sale of retired aircraft and costs of future leases. Given the large variety of miscellaneoustems, there is no clear driver of these expenses; however, a large portion of them, such asdvertising and promotional expenses, are driven by total revenue.

DISCUSSION QUESTIONS

1. Using the quarterly data for operating costs and the various cost drivers of costs providedby Exhibits 1 and 2, estimate regression for cost category of costs. Then, write theappropriate cost function for each category of cost and then interpret your regressionresults.

2. Based on your regression results, where do you see the largest reductions in costs if flyingcapacity is lowered by 11 percent? Also, in which areas do you see opportunities toachieve further cost reductions and why?

3. Exhibit 2 provides a quarterly forecast of revenues, jet fuel prices,6 and the projectedoperating activity for 2009. Using the information from your regressions and the forecastinformation provided in Exhibit 2, estimate Continental’s operating costs and expectedprofit for the upcoming fiscal year.

4. Based on the results of your profitability analysis, what can you say about the firm’sfinancial outlook? Would Continental be earning an operating profit in 2009? If not, whatshould Continental’s management do to restore profitability in 2009?

5. Summarize your conclusions in a memorandum addressed to Continental’s CEO. In thememo, you must clearly communicate your main findings, emphasizing specific areas inwhich you see the greatest potential to achieve further reductions in costs and, based onyour profitability analysis, sum up the financial outlook for 2009.

You should note that Continental has entered into several future contracts to hedge the exposed risks of rising fuelprices. The projected costs for jet fuel on exhibit reflects the value of the various future contracts which guaranteeContinental a fixed price for jet fuel at various maturity dates in 2009, as well the estimated gallons of fuel thatContinental plans to use during the year.


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CASE LEARNING OBJECTIVES AND IMPLEMENTATION GUIDANCECost estimation is a fundamental aspect of managerial/cost accounting �Datar et al. 2008;

ldenburg and Wolcott 2005�. For example, cost estimation is critical for developing budgets,etting up cost standards, inventory valuation, product costing, and many other applications. Ul-imately, firms’ ability to accurately predict production and operating costs has a profound impactn decision-making. Additionally, given the frequency with which firms downsize or expand theirperations in response to economic or market-wide conditions, knowing how this strategic deci-ion of scaling output impacts firms’ future operating costs, and which tools can facilitate this task,as become increasingly relevant for firms.

Nonetheless, despite its importance, cost estimation is a topic that merits further discussion inccounting textbooks. Although several managerial/cost accounting textbooks provide rich theo-etical discussions of cost estimation, including cost behavior, cost functions, and, to some extent,egression analyses, the examples that are typically used to illustrate such an important conceptften lack a sense of realism. Either fictitious data are commonly used in cost estimation, or thexamples covered fail to capture realistic situations faced by firms in a “real world” context.ccordingly, this case aims to close this gap.

The objective is to support students in learning how to apply regression analyses to under-tand cost behavior and forecast future costs using real data from firms. The case focuses on thearsh financial situation faced by Continental Airlines as a result of the recent financial crisis andhe challenges it faces to remain profitable. It then highlights the importance of reducing andontrolling costs as a viable strategy to restore profitability, and how regression analysis can assistn this pursuit. Students are next presented with quarterly data for various categories of costs andeveral potential cost drivers, which they must analyze and then perform regressions on operatingosts using a variety of cost drivers. Based on these results, students have to examine how costsehave and then use the regression output to forecast the firm’s operating costs for year 2009. Asart of the cost analysis, students must also identify specific areas in which Continental couldchieve the largest cost savings as a result of cutting capacity and implementing other cost-cuttingeasures. Apart from this, they must conduct a profitability analysis to project quarterly profits for

he upcoming fiscal year.The learning objectives of the case are as follows:

1. Students learn to conduct regression analysis in Excel and use this technique to study costbehavior and forecast future costs.

2. Students also learn how to use actual firm-level data from public sources for estimatingcosts, and apply cost estimation in a “real world” context that involves a widespreaddecision among firms: downsizing capacity. Moreover, learning to use public financialinformation in cost estimation could have implications that reach beyond accounting;learning to access public financial information exposes students to the possibilities ofapplying regression analysis for business analysis in general, including cost and profit-ability analyses.

3. The case requires students to synthesize their findings in a memorandum addressed toContinental’s CEO; thus, students are also exposed to refining their writing skills in abusiness setting.

mplementation GuidanceThis case is primarily designed for use in an intermediate managerial/cost accounting under-

raduate class; however, it could also work well in a graduate-level managerial accounting course,t either the master’s level or M.B.A.


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The realistic nature of the setting �everyone can easily identify with the business model ofirlines� makes a particularly appealing environment for students to learn how regression analysesan be applied in cost estimation in a real-world context. The questions presented in the casenclude both practical and theoretical questions. As an augmentation of the principles contained inhe application of this case, instructors could enhance the student experience by devoting time toeviewing the concepts of cost functions and cost estimation, as well as discussing the fundamen-als of regression analyses, so students can be exposed to these concepts prior to receiving thease. Alternatively, students can review these concepts on their own. The Appendix provides aetailed explanation of cost functions and regression analysis and describes the steps to performegression analysis in Excel. Additionally, it provides students with broad guidelines to write anffective memorandum.

tudent FeedbackThe case was administered to two sections of an upper-level intermediate undergraduate cost

ccounting class at a major U.S. university. Seventy-seven students responded to an evaluationurvey to assess whether they improved their understanding of the concepts illustrated in the case,s well as to whether the case illustrated a “real world” application in predicting operating costs.s shown in Table 1, students agreed that the case enhanced their understanding of the use of

egression analyses in predicting future costs �mean of 4.17, based on a five-point scale�, the casencouraged them to think critically about the behavior of operating costs in a “real world” contextmean of 4.03, based on a five-point scale�; plus, they found the case interesting and recommendedt for use in teaching cost estimation via regression analyses �mean of 4.07, based on a five-pointcale; see also Table 2�. Similar positive responses are shown in Table 2. For example, Table 2eports students’ knowledge on the use of regression analysis before and after working on the case.s shown, students significantly enhanced their knowledge on cost estimation after reading the

ase �mean 2.80 before and a mean of 4.47 after on a five-point scale�.

TABLE 1

Students’ Assessment of Case Learning Objectives

urvey QuestionsMean

(n � 58)Median(n � 58)

he case helped me improve myunderstanding of how regression analysiscan be used in predicting future costs.

4.17 4.00

he context of the assignment represents arealistic “real world” scenario.

4.29 4.00

he case taught me the necessary skills toperform regression analysis in Excel andto use such techniques to estimate futurecosts.

4.34 4.00

he case encouraged me to think criticallyabout the behavior of operating costs ina “real world” context.

4.03 4.00

found the case interesting and recommendits use for teaching cost estimation.

4.07 4.00

cale: 1 �strongly disagree� to 5 �strongly agree�.


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Additionally, students were asked to write specific comments about their experience workingn this case. Some of these comments are as follows:

• “Useful assignment, especially for students who are unskilled at statistics”• “This case improved my understanding of regression analysis and how to use it for pre-

dicting costs”

APPENDIXundamentals of Regression Analyses

Regression analysis is a powerful statistical technique that is commonly used to predict auture value of a variable of interest, such as costs, revenues, etc., based on data from the past. Toerform regression analysis, we must specify a regression model of the relationship between theariable of interest, the dependent variable, and one or several explanatory variables, the indepen-ent variables. One simple and frequently used way to describe the underlying relationship be-ween the dependent variable and the independent variable is with a linear regression model. Ainear model assumes that the relationship between the variables of interest is strictly linear and isescribed in the following way:

Y = a + bX + e.

o better illustrate, suppose that you want to estimate Aircraft Maintenance and Repair costs atontinental Airlines. In this case, the dependent variable is the underlying cost that we are trying

o predict and the independent variable is whatever factor causes that cost to rise or drop. Forxample, a common factor that tends to affect Aircraft Maintenance and Repair costs for airliness the number of aircraft in their fleet. Therefore, this would be the independent or predictorariable of Aircraft Maintenance and Repair costs.

In its most simple term, what the above model indicates is whether the variable Y is related to. More formally, the regression model represents the mean of Y for a given change in X. That is,hether the mean of Y is linearly related to X plus some error term.

Where Y represents the dependent variable �Aircraft Maintenance and Repair costs�, X is thendependent variable �number of aircraft�, a and b are the estimated coefficients, the constant andhe slope of the regression model, respectively, which will be explained next, and e is the residual

TABLE 2

Students’ Perception of Skills Proficiency Before and After the Case

urvey QuestionsMean Before

(n � 58)Mean After

(n � 58)

nderstanding of how regression analysesare conducted

2.81 4.47

nterpreting the regression output andapplying it in cost estimation

2.60 4.34

nderstanding the main statistics inregression

2.79 4.50

nderstanding the difference betweenunivariate and multivariate regression

2.43 4.24

nderstanding the advantage/limitations ofregression analysis for cost estimation

2.38 4.12

cale: 1 �least knowledgeable� to 5 �knowledgeable�.


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r estimated error of the model. The “a” coefficient is a value at which the line intercepts the-axis. It is the value of the mean of Y when X�0. With respect to a cost function, it represents

he fixed costs in the cost function. The “b” coefficient is called the slope because it measures thelope of the regression line. In our example, it represents the amount by which the mean of Ymaintenance costs� changes if X �number of the aircraft� changes by one unit.

The next question is how do we perform regression analysis in Excel? To explain the proce-ure, consider the following quarterly data for Continental Airlines for Aircraft Maintenance andepair costs and the potential cost driver of such costs, the number of aircraft in Continental’seet:

Observation Maintenance Costs No. of Aircraft

1 $340,000 102 $400,000 403 $440,000 504 $480,000 805 $530,000 110

he first step in regression analysis is to see whether a linear relationship exists between theependent variable and the independent �predictor� variable. This may be accomplished by plottinghe data on a graph. Data for maintenance costs, the dependent variable, is plotted on the Yvertical� axis, and data for the number of aircraft, the independent variable, on the X �horizontal�xis.

To create this graph in Excel, follow these steps:

1. Copy the data into an Excel spreadsheet, copying the data pertaining to the cost driver inthe first column and the cost data in the second column.

2. Click on the “Insert” menu bar and select the “Chart” option; alternatively, you may alsoselect the “Chart Wizard” toolbar.

3. Select the “XY �Scatter�” graph option.4. Select the cells that contain the data you want to use for your chart.5. Press “Next” to add a title and to label the Y and X axis.6. Press “Finish” to display the chart. Using the above data, the Scatter plot chart looks as

ollows:

Maintenance Costs vs. No. of Aircraft

$0

$100,000

$200,000

$300,000

$400,000

$500,000

$600,000

0 20 40 60 80 100 120

No. of Planes

As shown above, though the relationship scatter plots of Y and X will not outline a perfectlytraight line, we could observe that the scatter graph shows that this relationship is indeed linearnd, thus, indicates that the number of planes is a good predictor of Maintenance costs. Moreover,y plotting the data, we could identify potential outliers �data points that do not represent normalctivity� and eliminate them from the analysis. In this particular case, it can be observed that noutliers are present.


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Next, we’ll proceed to estimate the regression model, where maintenance costs would be aunction of the number of airplanes serviced. In this particular case, since we have a singleredictor variable, we refer to it as a univariate regression. The regression model is expressed asollows:

Maintenance costs = a + b�number of planes� + e

here maintenance costs substitutes for the Y variable and the number of planes for the X variableescribed previously; e is called the residual or error term and is defined as the difference betweenn actual observation of the dependent variable �cost� and its estimated or forecasted value fromhe regression estimation that we will run next.

The idea of regression analysis is to calculate the values of the dependent variables thatinimize the sum of square of these residuals. The mechanics of regression analysis work as

ollows: Using the data in your sample, regression would calculate a mean and would use thisean as a benchmark to compare as a central value in the calculations. Simply put, the regression

quation in this example will provide an estimate of the relationship between maintenance costsnd the number of flights that Continental Airlines offered, on average, in the quarter. Let us nowerform the regression in Excel.

To estimate a regression in Excel �MS Office 97 thru 2005 versions�, follow these steps:

1. On the “Tools” menu bar, select the “Data Analysis” command, and then select “regres-sion.” Note that if the “Data Analysis” command is not available, you need to install the“Analysis Toolpak add-in.” Here is how to add it: on the “Tools” menu bar, select the“Add-in” command. Then, select “Analysis Toolpak” and press “OK.”

2. Enter the cell reference for the dependent variable �i.e., maintenance costs�; the rangeselected must consist of a single column of data; then proceed to select the cell referencefor the independent variable �i.e., number of airplanes�.

3. Select if the first row or column of the input ranges contain labels �or headings�; clear ifyour input has no labels; Excel generates appropriate data labels for the regression outputtable.

4. Click to create a new worksheet containing the regression output.5. Press “OK” to generate the Regression Output Table.

o estimate a regression in Excel �MS Office 2007 version�, follow these steps:

1. On the “Data” menu bar, select the “Data Analysis” command located on the right handcorner, and then select “Regression Analysis.” Note that if the “Data Analysis” commandis not available, you need to install the “Analysis Toolpak add-in.” Here is how to add it:press the “Office” icon located on the left-hand side of the Excel menu bar, select the“Excel Option,” then select “Add-ins” command. Then, select “Analysis Toolpak” andpress “OK.”

2. Enter the cell reference for the dependent variable �i.e., maintenance costs�; the rangeselected must consist of a single column of data; then proceed to select the cell referencefor the independent variable �i.e., number of airplanes�.


4. Click to create a new worksheet containing the regression output.


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5. Press “OK” to generate the Regression Output Table.sing the above data, the Regression Output Table looks as follows:

Regression Statistics

Multiple R 0.990524645R2 0.981139072Adjusted R2 0.974852096Std Error 11566.62648Observations 5

NOVA

df SS MS F Significance F

egression 1 20878639456 20878639456 156.0589831 0.001105628esidual 3 401360544.2 133786848.1

otal 4 21280000000

CoefficientsStandard

Error t-stat p-value Lower 95% Upper 95%

ntercept 328707.483 10163.56279 32.3417575 6.49662E�05 296362.4902 361052.4758o. of Aircraft 1884.353741 150.8405294 12.49235699 0.001105628 1404.311856 2364.395627

et us now proceed to analyze each main statistic of the regression output. The intercept, with aalue of 328,707.48, is commonly referred to as the alpha coefficient and is a constant value in theegression function. In the specific case of cost estimation, this value represents the amount of fixedosts present in the cost function. In our example, this value indicates that $328,707 ofaintenance costs is fixed and would not change at all given any change in the number of planes

n Continental’s fleet. Further, the second statistic of interest is the coefficient estimate for theumber of Aircraft, which has a value of 1,884.35. This is the slope of the regression function and

t is referred as the beta coefficient. In cost estimation, this value represents the portion of variableosts in the cost function; that is, the portion of Maintenance costs that would vary given anyhanges in the number of planes. In our example, the slope coefficient is interpreted as follows: forach plane in which Continental provides scheduled maintenance, the amount of Maintenanceosts is expected to increase, on average, by $1,884.35 dollars.

An important issue in the examination of both coefficients is that neither the intercept nor thelope is ever examined in isolation. Each coefficient must be analyzed in combination with itsorresponding t-statistic, or the respective p-value. The t-statistic is the ratio of the value of theoefficient to its standard error. The standard error of the coefficient represents the amount ofariation in costs that is unexplained by the cost driver; in our example, the number of planes. Theower �higher� the standard error, the better �worse� each coefficient is. Going back to the-statistic, this value indicates whether each of the two coefficients is different from zero. A rule ofhumb is that if the t-statistic is at least 1.96 or greater, then we are 95 percent confident that thealue of the coefficient is significantly greater than zero. This implies that the coefficient is a validstimate and, therefore, could be used in the cost function as a way to predict future costs. If the-value is less than 1.96, then we cannot rule out the possibility that the coefficient is zero and,herefore, the coefficient should not be used in the prediction of costs. If we take a look at theegression Output Table, the t-statistics for the intercept and the slope coefficients are 32.34 and2.49, respectively. Therefore, we can conclude that both coefficients are not zero and, thereby, thestimates of fixed costs and variable costs per unit are valid estimates.

The second statistic of importance in the examination of each coefficient is the probability


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alue, or the p-value. Each t-statistic has a corresponding p-value. The p-value is the reciprocal ofhe t-statistic in that it tells us the probability that the coefficient estimate is significantly differentrom zero. If the p-value is less than or at least equal to 0.05, we are 95 percent confident that theoefficient estimate for the intercept or the slope coefficient is statistically significant differentrom zero. The smaller the p-value, the larger the t-statistic. For example, the p-value for the-statistic in the intercept coefficient equals 0.0000649, and 0.0011 for the slope coefficient. This

eans that both coefficients are not zero. More formally, this means that fixed costs have arobability of being zero about six in 100,000 times, and the variable cost about one in 1,000imes.

The last statistic of interest in our analysis is the R2, or the Adjusted R2. The R2 represents theoodness of fit of the model, or the explanatory power of the model. In the case of the Adjusted R2,t measures the same thing, but after adjusting for degrees of freedom in the regression model.imply put, the R2 �Adjusted R2� represents the percentage of variation in the dependent variablemaintenance costs� that is explained by the independent variable �number of planes�. This valueanges between 0 and 1, with the larger value representing a higher explained variation in costs. Inur example, the R2 equals 0.98, which indicates that approximately 98 percent of the variation inaintenance costs is explained by the number of planes which were serviced. A follow-up ques-

ion is “which cutoff value is acceptable?” This is subjective and will definitely depend on thenderlying analysis. In the case of cost estimation, perhaps we may want to have at least 30ercent of the variation in costs explained by the cost driver.

ultivariate Regression AnalysesThus far, we have explored regression analyses with one independent variable. Then, the

uestion becomes what happens if we have two or more independent variables �in our case, twoost drivers� as predictors of the dependent variable �Aircraft Maintenance and Repair costs�. Forxample, it is feasible that besides the number of planes in Continental’s fleet, Aircraft Mainte-ance and Repair costs may also be driven by other factors, such as the average utilization of eachlane in miles or hours.

In this case, the regression model varies subtly from the case of a single predictor variable.he mathematical notation for a multivariate regression model is expressed as follows:

Y = a + b1X1 + b2X2 + b2X2 + bkXk . . . + e

here Y represents the dependent variable �Aircraft Maintenance and Repair costs�, X1 is inde-endent variable one �number of aircraft in the fleet�, and X2 �average daily miles flown on eachircraft�. Just as in the case of a univariate regression, a and b are the estimated coefficients of theonstants and the slopes of the regression model; that is, the fixed costs and the slopes of the costunction, respectively, and e is the residual or estimated error of the regression model.

Assume the following quarterly data for Aircraft Maintenance and Repair costs and for eachf the potential cost drivers discussed above:

Observation Maintenance Costs No. of Aircraft Daily Miles Flown

1 $340,000 10 1,1662 $400,000 40 1,2703 $440,000 50 1,4574 $480,000 80 1,4505 $530,000 110 1,433

o estimate a multivariate regression in Excel, we must follow Step 1, described previously, plushe following additional steps:


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1. Enter the cell reference for the dependent variable �i.e., maintenance costs�; the rangeselected must consist of a single column of data.

2. Then, proceed to select the cell reference for all the independent variables �number ofairplanes, average age, and the average number of hours flown daily�; the range selectedmust consist of two or more columns. Note that all independent variables must be listedin sequential order; that is, next to each other.


4. Click to create a new worksheet containing the regression output.5. Press “OK” to generate the Regression Output Table.

sing the above data, the Regression Output Table for the multivariate regression looks as fol-ows:

Regression Statistics

Multiple R 0.999719472R2 0.999439023Adjusted R2 0.998878045Standard Error 2443.112692Observations 5

NOVA

df SS MS F Significance F

egression 2 21268062401 10634031200 1781.60298 0.000560977esidual 2 11937599.25 5968799.625

otal 4 21280000000

CoefficientsStandard

Error t-stat p-value Lower 95% Upper 95%

ntercept 147096.4284 22586.32684 6.512631708 0.022774575 49915.30752 244277.5492o. of Aircraft 1324.949381 76.23333561 17.38018375 0.003294134 996.943811 1652.95495aily Miles Flown 155.6548193 19.27060294 8.077319625 0.014983671 72.74010699 238.5695317

he statistics of interest are the same from the case of the univariate regression. The onlyifference is that now we have two slope coefficients. Let us summarize the main statistics ofnterest. The Intercept represents the alpha coefficient or the constant value in the regressionunction and, thus, indicates that there is $147,096 of fixed costs. The coefficient estimate for theumber of Aircraft has a value of $1,324.94 and indicates that for every aircraft in Continental’seet, Maintenance and Repair costs rise by this amount. And, the coefficient estimate for Dailyiles Flown indicates that for every mile that each aircraft in the fleet is flown daily, Maintenance

osts increase, on average, by $155. Note that the t-statistic for each coefficient is greater than 2nd, thus, indicates that both coefficients are different from zero. The Adjusted R2 indicates that 99ercent of the variation in Maintenance costs is explained by both cost drivers. An additionaltatistic of interest in multivariate regression is the F-statistic, which indicates the fitness of theodel; that is, how well all predictors as a whole explain changes in the dependent variable. An-statistic of two or greater indicates that the independent variables as a whole make a goodrediction of changes in Maintenance costs.

Let us now proceed to discuss how we can develop the cost functions as a way to estimateircraft Maintenance and Repair costs.


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evelopment of the Cost Function to Estimate Future CostsA cost function is simply an algebraic representation of how a given cost would change given

change in the cost driver. For example, in our example, the cost function would represent howircraft Maintenance and Repair costs would change given a one-unit change in the number of

ircraft. Using our prior results from the univariate regression, the cost function is written asollows:

Y = $328,707.48 + $1,324.94�X�

he above cost function indicates that $328,707.48 of Aircraft Maintenance and Repair costs isxed and would not change irrespective of changes in the number of aircraft in the fleet, andaintenance and Repair costs would rise by $ 1,884.35 for every aircraft in Continental’s fleet.

After setting up the cost function, we can now proceed to estimate Maintenance and Repairosts. Let us assume that Continental Airlines plans to have 142 planes in its fleet next year.lugging this figure into the cost function, we obtain the following result:

Y = $328,707.48 + $1,884.35�142�

Y = $596,285.18

herefore, given the projection with respect to the number of airplanes, we can conclude thatontinental Airlines would incur approximately $596,285.18 in Aircraft Maintenance and Repairosts for next year.

The same procedure is applied to the case in which there are multiple cost drivers. Referringo our prior results from the multivariate regression, the cost function is written as follows:

Y = $147,096.42 + $1,324.94�X1� + $155.65�X2�

sing the same projection as before of 142 planes for next year, plus assuming that the averagetilization of miles for each plane is 1,550 miles, then total Aircraft Maintenance costs for nextear is calculated as follows:

Y = $147,096.42 + $1,324.94�142� + $155.65�1,550�

Y = $576,495.40

ence, given the projection of the number of airplanes and the average utilization of miles thatontinental plans to fly each plane, we can conclude that Continental Airlines would incur ap-roximately $576,495 in Aircraft Maintenance and Repair costs for next year.

TEACHING NOTESTeaching Notes are available only to full-member subscribers to Issues in Accounting Edu-

ation through the American Accounting Association’s electronic publications system at http://aapubs.org/. Full-member subscribers should use their usernames and passwords for entry intohe system where the Teaching Notes can be reviewed and printed. Please do not make theeaching Notes available to students or post them on websites.

If you are a full member of AAA with a subscription to Issues in Accounting Education andave any trouble accessing this material, then please contact the AAA headquarters office [email protected] or �941� 921-7747.


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[email protected]

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REFERENCES

atar, S., C. Horngren, G. Foster, and C. Ittner. 2008. Cost Accounting: A Managerial Emphasis. 13thedition. Englewood Cliffs, NJ: Prentice Hall.

ldenburg, L., and S. Wolcott. 2005. Cost Management: Measuring, Monitoring, and Motivating Perfor-mance. 1st edition. New York, NY: John Wiley and Sons.

aynard, M. 2008. Big airlines in a rush go small. The New York Times �June 6�.


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a case study on cost estimation and pro_tability analysis at continental airlines.pdf

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