1974/09/01 by Franklin M. Henry · 61 citations
Mathematics · #Advanced Statistical Methods and Models #Computer science #Constant (computer programming) #Correlation #Econometrics #Linear regression #Mathematical analysis #Mathematics #Regression #Regression analysis #Series (stratigraphy) #Skewness #Statistical Methods and Applications #Statistical Methods in Clinical Trials #Statistics #Variable (mathematics)
paper · doi:10.1080/00222895.1974.10734991
published in Journal of Motor Behavior 6(3), 149-154 (Taylor & Francis)
openalex publication_date 1974/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/01/22
The correct individual single score for a within-S series of errors e about the target (for k trials) is E= √∑e(2) /k= √V(2) +C(2); the commonly used absolute error AE under-represents or deletes the variable error component V. In correlational analysis, the constant error score should be C absolute and V should be used unsquared in order to avoid curvilinearity; in general, r patterns across Ss are not predictable from within-Ss relations. While V and C are necessarily independent within Ss, they usually exhibit substantial correlation across Ss; evaluation of the role of each is sometimes important. Linearity of regression is demanded; it, rather than non-skewness, is shown to be the important assumption in using r. If relations involving algebraic C are of interest, the correlation index may be required because of U-shaped regression. Several common statistical misinterpretations are discussed.