dataset.airplane.2017

Commercial airplanes from 1965 to 2017.

Implements various decision support tools related to the Econometrics & Technometrics.
Subroutines include correlation reliability test, Mahalanobis distance measure for outlier detection, combinatorial search (all possible subset regression), non-parametric efficiency analysis measures: DDF (directional distance function), DEA (data envelopment analysis), HDF (hyperbolic distance function), SBM (slack-based measure), and SF (shortage function), benchmarking, Malmquist productivity analysis, risk analysis, technology adoption model, new product target setting, network DEA, dynamic DEA, intertemporal budgeting, etc.

DongJoon Lim

Distance Measure Based Judgment and Learning

dataset.airplane.2017 function

<code>[,1] Name </code>Airplane name 
 <code>[,2] EIS </code>Entry into service 
 <code>[,3] Range </code>Maximum range at full payload in 1,000km 
 <code>[,4] P.cap </code>Passenger capacity 
 <code>[,5] PFE </code>Passenger fuel efficiency in passengers*km/L (log scale) 
 <code>[,6] C.spd </code>Cruising speed in km/hr 
 <code>[,7] M.spd </code>Maximum speed in km/hr

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Author

Dataset of commercial airplanes from 1965 to 2017. — dataset.airplane.2017

<code>[,1] Name </code>Airplane name 
 <code>[,2] EIS </code>Entry into service 
 <code>[,3] Range </code>Maximum range at full payload in 1,000km 
 <code>[,4] P.cap </code>Passenger capacity 
 <code>[,5] PFE </code>Passenger fuel efficiency in passengers*km/L (log scale) 
 <code>[,6] C.spd </code>Cruising speed in km/hr 
 <code>[,7] M.spd </code>Maximum speed in km/hr

dataset.airplane.2017: Dataset of commercial airplanes from 1965 to 2017.

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Examples