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A Development of Multiple Regression for the Analysis of Routine Data

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  • R. G. Newton
  • D. J. Spurrell

Abstract

This paper presents a technique using quantities termed “elements” which are derived from a limited number of regression equations, and assist in the computations leading to tests of a regression model. It will be seen that special attention is given to “secondary elements” in choosing variables either for predicting the result arising from certain specified circumstances or for explaining the operation in terms of recognizably useful parameters. The meaning of these elements is argued geometrically, and their advantages in computation can easily be demonstrated; they have already helped to clarify complex industrial data.

Suggested Citation

  • R. G. Newton & D. J. Spurrell, 1967. "A Development of Multiple Regression for the Analysis of Routine Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 16(1), pages 51-64, March.
    • Handle: RePEc:bla:jorssc:v:16:y:1967:i:1:p:51-64
    DOI: 10.2307/2985237
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    Cited by:

    1. Norman Fickel, 2000. "Sequential Regression: A Neodescriptive Approach to Multicollinearity," Econometrics 0004009, University Library of Munich, Germany.
    2. Francesco Tosello & Andrea Guala & Dario Leone & Carlo Camporeale & Giulia Bruno & Luca Ridolfi & Franco Veglio & Alberto Milan, 2016. "Central Pressure Appraisal: Clinical Validation of a Subject-Specific Mathematical Model," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-10, March.
    3. Norman Fickel, 2001. "Sequential Regression: A Neodescriptive Approach to Multicollinearity," EERI Research Paper Series EERI_RP_2001_09, Economics and Econometrics Research Institute (EERI), Brussels.
    4. Fickel, Norman, 2000. "Sequential regression: a neodescriptive approach to multicollinearity," Discussion Papers 33/2000, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Statistics and Econometrics.

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