CORRELATIONS AS EIGENVALUES - AN APL FUNCTION S.K.Kazti and Bashir A. Khan situations. Hence, getting set up to handle cases where p and e m a y or m a y not equal one is important. A program, with a worked out example, is attached at the end. Let x and F be two matt,ides of size (Jr ,p ) and (N,Q). Since we consider the one, on line [4], we force X and we do the same to Y on means of both x and are of dimension P and 0 - W e every row of X a n d . possibility of p and/or Q being to be a two dimensional matrix line [2] of the program. Sample computed - they will be vectors subtract the m e a n vec tots from I. INTRODUCTION T h e Statistical profession recognizes four correlations as its central theme: (i) Karl Pearson's Corrrelation ( d . Dixon and Masse,/1969, p. 193), (ii) Coefficient of Determination (op cit p. 215), Off) Canonical Correlations (cf. Anderson 1984, p. 502) and (iv) Principal Components (op tit p. 453). It is recognized in all theoretical books that these corrspond to the
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Correlations as eigenvalues - an APL function
Katti, S. K.; Khan, Bashir A.
Follow ACM SIGAPL APL Quote Quad
, Volume 20 (1)
Association for Computing Machinery – Sep 1, 1989
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