Partial and multiple correlations and regressions: matrix calculations

Pierre Jolicoeur
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Pierre Jolicoeur: University of Montreal, Department of Biological Science

Chapter Chapter 25 in Introduction to Biometry, 1999, pp 213-222 from Springer

Abstract: Abstract Thanks to vectors and matrices (chapter 24), the partial and multiple correlations and regressions introduced in chapter 23 can still easily be used when there are more than two predictor variates. In order to broaden the discussion, let us subdivide each observed vector X into two subvectors X 1 and X 2, of which the elements are the predictor and the predicted variates respectively: $$ X = \left[ {X_1 \left| {X_2 } \right.} \right] = \left[ {X_1 \ldots X_i ,X_j \ldots X_k \left| {X_{\left( {k + 1} \right) \ldots } X_{u,} X_{v \ldots } X_q } \right.} \right]. $$

Keywords: Predictor Variate; Matrix Calculation; Head Length; Multiple Regression Equation; Estimate Covariance Matrix (search for similar items in EconPapers)
Date: 1999
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4615-4777-8_26

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DOI: 10.1007/978-1-4615-4777-8_26

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