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  • P C. Mahalanobis was an Indian scientist and applied statistician. He is best remembered for the Mahalanobis distance, a statistical measure. He made pioneering studies in anthropometry in India.
  • He founded the Indian Statistical Institute, and contributed to the design of large-scale sample surveys.
  • He went back to England and was introduced to the journal Biometrika. He discovered the utility of statistics to problems in meteorology, anthropology and began working on it on his journey back to India.                                        PRASANTA CHANDRA MAHALANOBIS

Mahalanobis Distance

  • A chance meeting with Nelson Annandale, then the director of the Zoological Survey of India, at the 1920 Nagpur session of the Indian Science Congress led to a problem in anthropology.
  • Annandale asked him to analyse anthropometric measurements of Anglo-Indians in Calcutta and this led to his first scientific paper in 1922. During the course of these studies he found a way of comparing and grouping populations using a multivariate distance measure.
  • This measure, D2, which is now named after him as Mahalanobis distance, is independent of measurement scale.
  • Inspired by Biometrika and mentored by Acharya Brajendra Nath Seal he started his statistical work. Initially he worked on analyzing university exam results, anthropometric measurements on Anglo-Indians of Calcutta and some meteorological problems.                                          PRASANTA CHANDRA MAHALANOBIS
  • He also worked as a meteorologist for some time. In 1924, when he was working on the probable error of results of agricultural experiments, he met Ronald Fisher, with whom he established a lifelong friendship. He also worked on schemes to prevent floods.

Sample surveys

  • The Mahalanobis distance is a descriptive statistic that provides a relative measure of a data point’s distance (residual) from a common point. It is a unitless measure.
  • The Mahalanobis distance is used to identify and gauge similarity of an unknown sample set to a known one.                    PRASANTA CHANDRA MAHALANOBIS
  • It differs from Euclidean distance in that it takes into account the correlations of the data set and is scale-invariant. In other words, it has a multivariate effect size.



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