Simple and Multiple Linear Regression
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Download the data file ex1.XLS. It contains a variety of aircraft operating cost data and statistics by aircraft type. All figures are averages for all aircraft operated by US carriers, taken from 2003 Form 41 data. Use the data in this file to perform the analysis of operating costs described below:
(A) Use Excel to estimate the following linear regression model with total flight operating costs per hour (HFC) as the dependent variable and number of seats as the explanatory (independent) variable:
HFC = a + b (SEATS)
Report the estimation results that you obtain, including the estimated constant (a) and parameter (b) and whether they are statistically significant (comment on the R-squared and t-statistic values). Interpret the meaning of the estimated parameter (b). Is there any evidence of economy of scale in this sample of aircraft types? Explain your answer.
(B) Now, estimate the following multiple regression model, adding the explanatory variables of average stage length (STAGE) and average utilization (UTIL):
HFC = a + b (SEATS) + c (STAGE) + d (UTIL)
Report the estimation results that you obtain, including the estimated constant (a) and parameters (b, c and d) and whether they are statistically significant (comment on the R-squared and t-statistic values).
Interpret the meaning of the estimated slope parameters (b, c and d) and discuss whether the estimation results match your a priori expectations of the relationships between the dependent variable and each explanatory variable. That is, does the sign of each parameter make sense? Explain.
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Solution Summary
The solution uses EXCEL to
1) estimate the linear regression model with total flight operating costs per hour (HFC) as the dependent variable and number of seats (SEATS) as the explanatory (independent) variable.
2) estimate the multiple regression model, with total flight operating costs per hour (HFC) as the dependent variable and number of seats (SEATS) , average stage length (STAGE) and average utilization (UTIL) as the explanatory (independent) variables.
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