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Fisher and Regression
Vol. 20, No. 4 (Nov., 2005), pp. 401-417
Published by: Institute of Mathematical Statistics
Stable URL: http://www.jstor.org/stable/20061201
Page Count: 17
You can always find the topics here!Topics: Statistics, Least squares, Statism, Linear regression, Inference, Correlations, Statistical estimation, Regression coefficients, Goodness of fit, Applied statistics
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In 1922 R. A. Fisher introduced the modern regression model, synthesizing the regression theory of Pearson and Yule and the least squares theory of Gauss. The innovation was based on Fisher's realization that the distribution associated with the regression coefficient was unaffected by the distribution of X. Subsequently Fisher interpreted the fixed X assumption in terms of his notion of ancillarity. This paper considers these developments against the background of the development of statistical theory in the early twentieth century.
Statistical Science © 2005 Institute of Mathematical Statistics