Karl pearson:

Karl pearson:

	Born 27 March 1857 Islington, London, England Died 27 April 1936 (aged 79) Coldharbour, Surrey, England Residence England Nationality British Fields Lawyer, Germanist, eugenicist, mathematician and statistician (primarily the latter) Institutions University College London King's College, Cambridge Alma mater University of Cambridge University of Heidelberg Academic advisors Francis Galton Notable students Philip Hall John Wishart Known for Pearson distribution Pearson's r Pearson's chi-squared test Phi coefficient Influenced Henry Ludwell Moore Notable awards Darwin Medal (1898) Contributions to statistics : Correlation coefficient. The correlation coefficient (first conceived by Francis Galton) was defined as a product-moment, and its relationship with linear regression was studied.[10] Method of moments. Pearson introduced moments, a concept borrowed from physics, as descriptive statistics and for the fitting of distributions to samples. Pearson's system of continuous curves. A system of continuous univariate probability distributions that came to form the basis of the now conventional continuous probability distributions. Since the system is complete up to the fourth moment, it is a powerful complement to the Pearsonian method of moments. Chi distance. A precursor and special case of the Mahalanobis distance.[11] P-value. Defined as the probability measure of the complement of the ball with the hypothesized value as center point and chi distance as radius.[11] Foundations of the statistical hypothesis testing theory and the statistical decision theory.[11] In the seminal "On the criterion..." paper,[11] Pearson proposed testing the validity of hypothesized values by evaluating the chi distance between the hypothesized and the empirically observed values via the p-value, which was proposed in the same paper. The use of preset evidence criteria, so called alpha type-I error probabilities, was later proposed by Jerzy Neyman and Egon Pearson.[12] Pearson's chi-squared test. A hypothesis test using normal approximation for discrete data. Principal component analysis. The method of fitting a linear subspace to multivariate data by minimizing the chi distances.[13][14] In the course of his studies of race, Pearson devised a Coefficient of Racial Likeness, calculated from several measurements of the human skull.