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Principal Component Analysis

In: Statistical Methods in Social Science Research

Author

Listed:
  • S. P. Mukherjee

    (University of Calcutta, Department of Statistics)

  • Bikas K. Sinha

    (Indian Statistical Institute)

  • Asis Kumar Chattopadhyay

    (University of Calcutta, Department of Statistics)

Abstract

Principal component analysis (PCA) is a method for dimension reduction tool in order to reduce a large set of variables to a small set of components that still contains most of the information in the original data set. Under PCS, we transform a number of correlated variables into a smaller set of uncorrelated components called principal components.

Suggested Citation

  • S. P. Mukherjee & Bikas K. Sinha & Asis Kumar Chattopadhyay, 2018. "Principal Component Analysis," Springer Books, in: Statistical Methods in Social Science Research, chapter 0, pages 95-102, Springer.
    • Handle: RePEc:spr:sprchp:978-981-13-2146-7_9
    DOI: 10.1007/978-981-13-2146-7_9
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