A measured value for the degree of the relation between two sets of data. The correlation coefficient r varies between -1 and 1. Positive correlations (between 0 and 1) indicate that high values in one set of data (e. g. the numerical result in a selection test) are related to high values in another set of data (e. g. an indicator for job success).
A statistical measure of the extent to which two variables are associated.
Statistical procedure for estimating the relationship between two variables, x and y
A numerical measure of the extent to which the returns of two assets move together over time. The range is from -1.0 to +1.0.
A statistic that measures the degree to which two variables are related.
When two random variables X and Y tend to vary together. The measurement is given by the ratio of the covariance of X and T to the square root of the product of the variance of X and the variance of Y.
Measurement (between -1 and 1) of the quality of fit of a line through a set of data points. The closer the number to +-1.0 the better the fit.
A numerical value that identifies the strength of relationship between variables.
A correlation coefficient is a measure of the interdependence between two variables. It is usually a pure number which varies between -1.0 and +1.0, with the intermediate value of zero indicating an absence of correlation. The limiting values indicate perfect negative or positive correlation.
A measure of the strength of the relationship between two interval data variables.
A measure of the amount of linear relationship between 2 variables, which takes values from -1 (perfect negative relationship, i.e. as one increases the other decreases) to +1 (perfect positive relationship). If the variables are independent the correlation coefficient is 0, but the reverse is not necessarily true.
A statistical measure of the association between two variables.
The extent to which the individual pressure and flow readings correspond to an average curve. A measure of the accuracy of the test—should be above—99.
It is a standardised statistical measure, which describes the relationship of two or more variables with each other.
A statistical index of the degree of relationship between two variables. Values of correlation coefficients range from -1.00 through zero to +1.00. A correlation coefficient of 0.00 indicates no relationship between the variables. Correlations approaching -1.00 or +1.00 indicate strong relationships between the variables. However, causal inferences about the relationship between two variables can never be made on the basis of correlation coefficients, no matter how strong a relationship is indicated.
In statistical analysis, the strength between two sets of data. Zero indicates no relationship, Negative indicates an inverse relationship.
A standardized statistical measure of the dependence of two random variables, defined as the covariance divided by the standard deviations of two variables.
(r): A measure of degree of association among two or more variables. When knowing the value of one variable affords little or no advantage over chance of predicting another variable, the variables are independent and the correlation coefficient is close to 0.0 (weak or null correlation). When knowing one variable allows prediction of the other, the r-value gets closer to -1.0 or to 1.0. [See negative, Pearson, Kendall, point-biserial, phi, Cronbachâ€™s alpha, Cohenâ€™s Kappa, partial, and spurious correlation, covariance, reliability, validity, attenuation, correlation matrix
A number between -1 and 1 that measures how closely two variables are related. The closer to -1 or 1, the stronger the relationship. A negative correlation means that as one value goes up, the other goes down (example: points allowed and wins). Values close to zero suggest no correlation between the two variables. (Note: while a strong correlation coefficient suggests that one variable affects another, correlation does not necessarily indicate causation.)
a statistic representing how closely two variables co-vary; it can vary from -1 (perfect negative correlation) through 0 (no correlation) to +1 (perfect positive correlation); "what is the correlation between those two variables?"
a measure of the interdependence between two variates or variables
a measure of the linear relationship between two samples
an inappropriate numerical summary of this data
a numerical, descriptive measure of the strength of the linear relationship between two variables
a numerical index of that relationship
A measure of how closely the points on a scatter diagram approximate a non-horizontal, non-vertical straight line. The correlation coefficient lies between 1 and -1, with 1 for a perfect increasing line and -1 for a perfect decreasing line. Numerically, it is equal to the product of the regression coefficient and the ratio of the standard deviations of the explanatory and response variables, respectively.
A statistical measure of the direction and magnitude that one variable moves when a second variable moves upwards. If variable A moves up when variable B moves up, A and B are positively correlated and the coefficient will be positive. If variable A moves down when variable B moves up, A and B are negatively correlated and the coefficient will be negative. If A moves up and B doesn't move at all, the variables are uncorrelated (independent) and the coefficient is zero.
Degree to which two series of numbers plot as a straight line. A correlation coefficient of 1 (or -1) indicates that the two series of numbers plot exactly along a straight line. A correlation coefficient of zero indicates that there is no straight line relationship between the two series of numbers. As applied to two portfolios, a high correlation coefficient for the relative returns indicates that the portfolio values have moved in tandem and a low correlation coefficient means the opposite. When the correlation coefficient is high, one portfolio could have been used as a surrogate or a hedge for the other.
The customary index for expressing the degree of relationship observed between two sets of measures for the same group. The coefficient can range from -1.00, showing perfect negative correlation, through zero, indicating no correlation, to +1.00, showing perfect positive correlation. If, for example, the correlation coefficient between height and weight for a group of men were +1.00, knowing a man's height would permit you to predict his weight without error. But if the correlation coefficient between height and weight were zero, you could not predict a man's weight by knowing his height any more accurately than by not knowing his height. Most correlation coefficients of test scores and measures of academic success fall somewhere between zero and +1.00. Knowledge of an individual's score on one variable enables you to predict that individual's standing on the other variable with greater accuracy than if the correlation were zero. The higher the coefficient, the lower likelihood of error in prediction.
A statistical measure of the extent to which variations in one variable are related to variations in another.
A statistical measure of the direction and strength of the linear relationship between two variables, which can range from -1.00 to +1.00.
A number between - I and + I which is a measure of the dispersion of data points about a regression line.
A statistical measure of the degree to which the movements of two variables are related. A correlation coefficient of +1 means they move exactly together. A value of -1 means they move exactly opposite to each other. A value of 0 means the two variables move independently of each other.
This is a measure of the degree to which two variables are related by a simple linear relationship. With blasting, the variables are the log values of PPV and Scaled Distance. A value of 1 gives a perfect relationship and a value of 0 gives no relationship.
The correlation coefficient measures the degree to which the movements of two variables are related. It indicates how close the residuals are to the regression line and is calculated as the square root of the coefficient of determination. covariancex,y(Î²x) (Î²y) (Ïƒ2market) = = (Ïƒx) (Ïƒy)(Ïƒx) (Ïƒy) Ïƒx = standard deviation of "x" Ïƒy = standard deviation of "y" Î²x = beta of "x" Î²y = beta of "y" Ïƒ2market = variance of the market portfolio
Relationship between two variables. The correlation coefficient measures the degree to which two variables are related. The measure is often used to determine if mutual fund returns vary based on market conditions or on other fund categories. Portfolio diversification is enhanced where funds are not highly correlated.
A correlation coefficient is a number between -1 and 1, which measures the degree to which two variables are linearly related. If there is perfect linear relationship with positive slope between the two variables, we have a correlation coefficient of 1; if there is positive correlation, whenever one variable has a high (low) value, so does the other. If there is a perfect linear relationship with negative slope between the two variables, we have a correlation coefficient of -1; if there is negative correlation, whenever one variable has a high (low) value, the other has a low (high) value. A correlation coefficient of 0 means that there is no linear relationship between the variables.
A mathematical index of the degree of association between two or more measures.
A measure of the degree of relationship between two sets of measures (such as results from two tests that were given to a group of individuals). Correlation coefficients range from -1.00 to 0.00 to +1.00 where -1.00 indicates a perfect negative relationship, 0.00 indicates no relationship and +1.00 indicates a perfect positive relationship.
( Stat.). A measure of liner correlation that can take values between + 1 and - 1. A value near to + 1 indicates almost perfect positive association, high values of the other; a value near to -1 indicates almost perfect negative association; a value near to zero indicates absence of association. ( BCFT).
A statistic used for quantifying the strength of a linear association between variable inputs and outputs. It ranges from +1 (perfect positive correlation: higher input goes with higher output) to -1 (perfect negative correlation: higher input goes with lower output).
A number that expresses both the size and the direction of a correlation, varying from +1.00 (perfect positive correlation) through 0.00 (absence of any correlation) to -1.00 (perfect negative correlation).
a type of similarity coefficient usable with all types of data. A measure of the correlation of the character states of one OTU to those of the other. Maximum similarity = 1.0; minimum similarity = -1.0.
A measure of how closely a best-fit curve matches the given data.
Statistic that measures the degree of linear association between two variables. Its values vary from between -1 and 1. Perfect positive (the dependent variable increases with an increase in the independent variable) linear association has a correlation coefficient of 1. Perfect negative (the dependent variable decreases with an increase in the independent variable) linear association has a correlation coefficient of -1. Absolutely no association between variables has a value of zero.
An index number giving the relationship between a predictor and a criterion variable.
A mathematical measure of how much one number (such as a share price) is influenced by changes in another (such as an index).... more on Correlation coefficient
A statistical measure that relates how well a set of data points can be modeled by a line.
A measure of the closeness of the relationship between 2 variables.
See correlation. 2. A measure of the persistence of the eddy velocity as a function of time and space. Two types are distinguished: 1) In the Eulerian correlation coefficient, the time difference is zero, where â€² is the eddy velocity. For homogeneous and homologous turbulence, this correlation tensor depends only on the difference ( 1); when the turbulence is isotropic, the tensor is spherically symmetric and . 2) In the Lagrangian correlation coefficient, time and space are varied together in such a way that the same fluid parcel is being followed: When the flow is one-dimensional and the mean velocity is much greater than the eddy velocity, then a fixed point experiences approximately the same sequence of fluctuations as a fluid parcel. The Lagrangian correlation coefficient can then be converted into the Eulerian by a proper scaling. These correlation coefficients have the same form and meaning when any other fluctuating quantity is used, for example, temperature or pressure.