The results are analysed using Canonical correlation analysis (CCA).
📌 3 Key Takeaways
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Objective & Hypothesis:In this study , Canonical correlation analysis (CCA) and Data envelopment analysis (DEA) techniques are used.
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Key Findings & Data:Canonical correlation analysis (CCA) is a multivariate statistical technique that can be used to determine the relationship between two multiple variable sets.
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Conclusion & Impact:The results are analysed using Canonical correlation analysis (CCA).
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Canonical Correlation Analysis · Data Envelopment Analysis · Efficiency Evaluation.
Abstract
In this study , Canonical correlation analysis (CCA) and Data envelopment analysis (DEA) techniques are used. Canonical correlation analysis (CCA) is a multivariate statistical technique that can be used to determine the relationship between two multiple variable sets. Data envelopment analysis (DEA) is a linear programming (LP) technique for measuring the relative efficiency of peer decision making units(DMUs) when multiple inputs and outputs are present. A beneficial method for model selection is proposed in this study. Efficiencies are calculated for all possible DEA model specifications. It is shown that model equivalence or dissimilarity can be easily assessed using this approach. The results are analysed using Canonical correlation analysis (CCA).
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