dataset.engine.2015

Auto engines from MY2005 to MY2015.

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.engine.2015 function

<code>[,1] Name </code>Vehicle name 
 <code>[,2] MY </code>Model year 
 <code>[,3] Cylinder </code>The number of cylinder 
 <code>[,4] Displacement </code>Displacement in liter 
 <code>[,5] CO2 </code>CO2 emission in gram/mile 
 <code>[,6] Power </code>Engine power in HP 
 <code>[,7] Torque </code>Engine torque in lb.ft 
 <code>[,8] Type </code>Engine system and fuel type

Columns

Author

Dataset of auto engines from MY2005 to MY2015. — dataset.engine.2015

<code>[,1] Name </code>Vehicle name 
 <code>[,2] MY </code>Model year 
 <code>[,3] Cylinder </code>The number of cylinder 
 <code>[,4] Displacement </code>Displacement in liter 
 <code>[,5] CO2 </code>CO2 emission in gram/mile 
 <code>[,6] Power </code>Engine power in HP 
 <code>[,7] Torque </code>Engine torque in lb.ft 
 <code>[,8] Type </code>Engine system and fuel type

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dataset.engine.2015: Dataset of auto engines from MY2005 to MY2015.

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Author

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Examples