8/14/2019 Case 3: Demand Estimation

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Case 3: Demand Estimation

The global aerospace industry is dominated by Boeing and McDonnell Douglas fromthe United States, and Airbus Industrie, the European consortium. During the early-1990s,the end of the cold war with the former soviet Union led to a dramatic downshifting inorders for military-related purchases at the same time a global recession cut sharply intothe commercial demand for aircraft. Against this backdrop, a number of analysts began toquestion the wisdom and profitabili ty of the industrys massive long -term investments inresearch and development (R&D).

The following table shows sales revenue, profit, and R&D data for an n=19 sample of firms taken from the U.S. aerospace industry. Data are for the fiscal year reported as of May18, 2005 and limited to those companies reporting sales of $58 million or more and R&Dexpenditures of at $1 million. R&D expenses are the dollar amount of company-sponsoredR&D as reported to the Securities and Exchange Commission on Form 10-K. Excluded fromsuch numbers is R&D under contract to others, such as U.S. government agencies. Allfigures are in $ millions.

Company Name Sales ($) Profits ($) R&D Expense ($)

Abex 728.4 (194.0) 1.6

Boeing 30,184.0 2,256.0 1,846.0

Curtiss-Wright 179.7 32.7 1.6

GenCorp 1,937.0 37.0 36.0

General Dynamics 3,472.0 227.0 66.0

K&F Industries 295.5 (14.7) 14.1

Kaman 782.9 29.0 17.8Lockheed 10,100.0 549.0 420.0

Martin Marietta 5,954.3 512.4 200.0

McDonnell Douglas 17,373.0 1,086.0 509.0

Northrop 5,550.0 180.0 93.0

OEA 88.1 23.1 2.6

Orbital Sciences 174.6 5.4 6.0

Pacific Scientific 172.6 8.1 8.2

Sequa 1,868.3 43.8 17.6

Sunstrand 1,672.7 130.1 115.4

Thiokol 1,311.7 101.8 14.2United Technologies 21,641.0 200.0 1,219.0

Woodward Governor 374.2 33.0 16.0

Averages 5,466.3 276.1 242.8

A. A simple regression model with sales revenue as the dependent Y-variable and R&Dexpenditures as the independent X-variable yields the following results:

8/14/2019 Case 3: Demand Estimation

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Constant $1,364.797Std. Err. of Y Est. $2,259.882R-squared 93.40%No. of Observations 19Degrees of Freedom 17

R&DX Coefficient $16.889Std. Err. Of Coef. 1.093t-statistic 15.46

How would you interpret these findings?

B. A simple regression model with net income (profits) as the dependent Y-variable andR&D expenditures as the independent X-variable yields the following results:

Constant $45.073Std. Err. of Y Est. $317.105R-squared 69.40%No. of Observations 19Degrees of Freedom 17

R&DX Coefficient $0.951Std. Err. Of Coef. 0.153t-statistic 6.20

How would you interpret these findings?C. Discuss any differences between your answers to Part A and B.