(redirected from multiple correlation)
Also found in: Dictionary, Thesaurus, Medical, Legal, Encyclopedia, Wikipedia.
Related to multiple correlation: partial correlation, multiple correlation coefficient


Statistical measure of the degree to which the movements of two variables (stock/option/convertible prices or returns) are related. See: Correlation coefficient.


The relationship between two variables during a period of time, especially one that shows a close match between the variables' movements. For example, all utility stocks tend to have a high degree of correlation because their share prices are influenced by the same forces. Conversely, gold stock price movements are not closely correlated with utility stock price movements because the two are influenced by very different factors. The concept of correlation is frequently used in portfolio analysis. See also serial correlation.


In investment terms, correlation is the extent to which the values of different types of investments move in tandem with one another in response to changing economic and market conditions.

Correlation is measured on a scale of - 1 to +1. Investments with a correlation of + 0.5 or more tend to rise and fall in value at the same time. Investments with a negative correlation of - 0.5 to - 1 are more likely to gain or lose value in opposing cycles.


a statistical term that describes the degree of association between two variables. When two variables tend to change together, then they are said to be correlated, and the extent to which they are correlated is measured by means of the CORRELATION COEFFICIENT.


A former appraisal term, replaced by reconciliation.
References in periodicals archive ?
With respect to the SAT, no official composite score is reported, and validity studies focus on the multiple correlations of the scores in the three SAT sections with the FYGPA criterion.
In view of the importance of accurate sample size formulas for a precise confidence interval of the squared multiple correlation coefficient and computational demands of the current methods, Bonett and Wright (2011) proposed a simple procedure of approximating the sample size requirement for obtaining a squared multiple correlation confidence interval with desired precision.
This is because between multiple variables, multiple dependent variables often exist in multiple correlations, namely multi collinearity partial least squares regression analysis.
The Multiple correlation between organizational climate and job satisfaction showed that the three factors of organizational climate (purpose, role and bonuses) and the two factors of job satisfaction (labor, management) are the best predictors of job satisfaction of PE teachers.
Checking the accuracy of the multiple regression models and of the multiple correlation ratios, based on "Fisher" criterion, it leads to the following conclusion that for the significance level of 5% the multiple regression models is valid.
When planning a multiple regression analysis, it is important to obtain a sample size that is large enough to provide an acceptably narrow confidence interval for important population parameters such as the squared multiple correlation, denoted here as [[rho].
Results in table 3, shows that experience of teachers and self efficacy beliefs has a multiple correlation of 0.
For obtaining the ratings of informative criteria we investigated the techniques of receiving coefficients of twin-, private-, auto-, multiple correlation and parameters of regression model for time series.
Multiple correlation testings between different variables can be pricey
076 QTmax = maximal QT interval duration in all measurable leads, QTc = heart rate corrected, QTm = the mean QT interval duration in all leads, QTd = QT interval dispersion, QTII = the QT interval in lead DII, QTcII = heart rate corrected QT interval in lead DII, T0e = maximal T-wave duration in all measurable leads, Tpe = maximal Tpeak-Tend interval in all measurable leads, Ta = maximal T-wave amplitude in all leads, multiple R = multiple correlation coefficient, R square = coefficient of determination, adjusted R = the coefficient of determination adjusted for the number of independent variables in the regression model Table 4: Linear regression analysis.
Multiple correlation based on P coefficient can only evaluate a limited multi-linear relationship.

Full browser ?