Reciprocal relation; corresponding similarity or parallelism of relation or law; capacity of being converted into, or of giving place to, one another, under certain conditions; as, the correlation of forces, or of zymotic diseases.
Correlation measures how two assets' returns move together. Two assets that are perfectly negatively correlated (-1) tend to simultaneously move in opposite directions. Two assets that are perfectly positively correlated (+1) tend to simultaneously move in the same direction. A correlation of 0 indicates that there is no relationship at all between the price movements of two assets.
Correlation is a statistical measure of the extent to which the movement of two prices are related. A positively correlated pair of prices move in a similar way e.g. as one rises so does the other. A negatively correlated pair of prices move in a disimilar way e.g. as one rises the other falls. Correlation is important because it helps identify assets which, when combined with other assets, can deliver diversification benefits. Perfectly positively or perfectly negatively correlated prices are rare but in any case where there is less than perfect correlation some diversification benefits will be realised.
see also Arbitrage) Correlation is a statistical measure describing the extent to which prices on different instruments move together over time. Correlation can be positive or negative. Instruments that move together in the same direction to the same extent have highly positive correlations. Instruments that move together in opposite direction to the same extent have highly negative correlations. Correlation between instruments is not stable.
Correlation between two measurements or series of numbers is one way of measuring the extent to which these numbers have a tendency to move together. A correlation of +1 indicates that the two series of number always move together, 0 that they don't, and –1 that they move in an opposing sense. As an example, Average Temperature and the consumption of ice cream may well have a positive correlation, but Average Temperature and the sale of telephones would probably have a zero correlation.
A statistical term that refers to a relationship between two seemingly independent things. In forex for example, one could argue that the Euro and the Sterling have a higher correlation than for example the Euro and the Brazilian Real.
is a term used to refer to the observed association between two or more variables. The higher a correlation between the variables the greater the likelihood that they are associated in some way (e.g., many prostitutes have a history of sexual abuse). Criminology seeks to find correlations between a crime and possible related factors or characteristics.
statistical term that describes the tendency of two elements to move in relationship to one another; a positive correlation describes one element going up along with the other; a negative means one goes up and the other goes down. Does not imply causality.
The process of establishing a relation between a variable and one or more related variables. Correlation is simple if there is only one independent variable; multiple, if there is more than one independent variable. For gaging station records, the usual variables are the short-term gaging-station record and one or more long-term gaging-station records.
A measure of association or co-movement between two variables. If two variables tend to rise and fall together, they have a positive correlation. If one is rising while the other is declining, they have a negative correlation.
The degree to which a pair of investments moves in the same direction with the same impact on performance. A perfectly correlated pair of investments has a correlation factor of 1.00. A random correlation (no linkage) is zero. A negative correlation means performance moves in opposite directions.
in social statistics this term means the same as association, referring to a situation where two variables vary together. Amongst other things an association or correlation may be positive (in which case the two variables rise together) or negative (where one goes down the other goes up). Correlation coefficients (or tests of association) exist to indicate the strength and direction of linear relationships like this.
a mathematical combination of the electrical signals from two radio antennae resulting in the basic measurement of a radio interferometer (called a visibility ). Many correlations from many pairs of antennae are combined to produce a radio image of the sky. (See also text in computation, and baselines.)
The degree to which two or more sets of measurements vary together; e.g., a positive correlation exists when high values on one scale are associated with high values on another; a negative correlation exists when high values on one scale are associated with low values on another.
A type of relationship between two answers to two questions. For example, there is a correlation between people's height and their weight: other things being equal, taller people weigh more than shorter people. A negative correlation occurs when one thing gets smaller as another gets bigger. See also similarity.
A measure ranging from 0.00 to 1.00, of how well two or more things (“variables,” values, scores, etc.) change together. Both things may get higher at the same time, or lower at the same time, or one may get higher while the other gets lower. For example, saving money and spending money are correlated (inversely), because the more money you save, the less you spend.
The amount of positive or negative relationship existing between two measures. For example, if the height and weight of a set of individuals were measured, it could be said that there is a positive correlation between height and weight if the data showed that larger weights tended to be paired with larger heights and smaller weights tended to be paired with smaller heights. The stronger those tendencies, the larger the measure of correlation.
Any of a number of statistical measures that describe the degree to which the prices of two or more instruments trade in tandem. Measures the degree to which their prices move in the same direction and to the same extent.
The fact that two variables systematically evolve in the same direction or in opposite directions. If there is little covariance, there is no statistically reliable relationship between them. A large degree of covariance between A and B indicates an assumption of causality but does not prove it. (Is A the cause of B? or is B the cause of A? or are A and B the consequence of something else?). Related Terms: Covariation, Spurious correlation BACK
correlation measures the degree to which two variables are related. For example, how much is the degree to which people know their neighbors related to the number of years they have lived in the neighborhood. Correlation scores range between -1 and 1. If the relationship is greater than zero there is positive correlation, i.e., more of the first variable is associated with more of the second variable. If the relationship is less than zero there is negative correlation, i.e., more of the first variable is associated with less of the second variable. Finally, items that have correlations closer to 1 or closer to -1 are more strongly correlated (i.e., the relationship between the variables is stronger), and items that have correlations closer to 0 are more weakly correlated (i.e., the relationship between the variables is weaker).
An association between two, or more than two, variables, of such a nature that a change in one seems to tied or related to a change in another. A correlation among variables does not necessarily mean that they are causally linked.
The correlation coefficient (r estimates rho) provides an index of the degree to which paired measures(X and Y) co-vary in a linear fashion. Its values is constrained to lie between -1 and +1. r is positive ( 0) when cases with large values of X also tend to have large values of Y whereas cases with small values of X tend to have small values of Y. r is negative ( 0) when cases with large values of X tend to have small values of Y and vice versa. Correlation coefficients give no information about cause and effect. Similarly they provide misleading information if the relationship between X and Y is non-linear.
A statistic that shows the degree of relationship between variables. Higher correlations (.70, .80, .90) indicate that a strong relationship is present between variables. A positive correlation means that high scores on one variable are associated with high scores on a second variable. A negative correlation means that high scores on one variables are associated with low scores on a second variable.
a term meaning association or relationship, often used for numerical data. A measure of correlation for numerical data is derived from a comparison of the regression line and the bivariate data in a scatter plot.
A form of statistical modelling that attempts to summarise how one dataset will vary in response to another. A correlation coefficient of +1.0 means that where there are high values in one set there will be high values in the other, while a correlation coefficient of -1.0 means that where there are high values in one set there will be low values in the other. A correlation coefficient of 0.0 means that there is no discernible relationship between the two sets. This is a form of global analysis as it only provides a single summary statistic for the entire study area.
The extent to which two variables vary together (either in a positive or negative relationship). A positive correlation exists when one variable increases as the other increases. A negative correlation exists when one variable decreases as the other increases. For example, a positive correlation may exist between level of income and credit rating. A negative correlation may exist between level of income and rate of mortgage default. A fundamental principle of statistics is that correlation does not necessarily imply causation. This is easy to forget in the quest to understand relationships between different indicators. An easy way to remember this fallacy: a positive correlation may exist between the amount of graffiti in a neighborhood and the level of violent crime in that neighborhood, but the graffiti does not necessarily cause the violent crime and the absence of graffiti does not guarantee there will be no violent crime in the neighborhood. In this scenario, additional variables such as employment rate, education level, and police services have to be considered. (See Linkage.)
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?"
Two variables are correlated when movements in the two variables tend to occur together. With a positive correlation, one variable tends to be above its average value when the other one is above its average valueâ€”that is, the two variables tend to move in the same direction. With a negative correlation, one variable tends to be above its average value when the other one is below its average valueâ€”that is, the two variables tend to move in opposite directions.
A standardised measure of the relative movement between two variables, be they securities, indices or funds. Two assets are said to be perfectly correlated if their prices move up and down in perfect tandem and by the same amount. Two assets are said to be “perfectly negatively correlated” if they move by the same amount in opposite directions.
Coefficient that measures the linear relationship between two series of data (e.g. equities and an index). By definition, the correlation ranges from +1 to â€“1; a value of +1 means that the index and the stock move in the same direction, whereas a correlation of â€“1 means they move in opposite directions. Combining two investments with a correlation less than 1 allows the risk to be reduced.
The tendency for two measures or variables, such as height and weight, to vary together or be related for individuals in a group. If, as in the case of height and weight, people who are high on one variable (tall) tend to be high on the other (heavy), the correlation is said to be positive. As another example, months of practice and golf scores would have negative correlation; for, ordinarily, as the first variable is high (practice increases), the second tends to be low (score decreases as golfer improves). Correlation implies only association, not cause.
A statistical measure of the relationship between price movements. For example, an Absolute Return Fund may have a positive or negative correlation with the market. Correlation is measured by R-squared.
A measure of association or relationship between two variables that indicates the strength (strong, moderate, weak) of the association and whether the association is positive or negative. A simplified example of a positive correlation: As poverty increase, crime increases. A simplified example of negative correlation: As physical activity increases, cholesterol decreases. However, correlation is not causation. In other words, it tells us a relationship can be observed, but it does not tell us whether a change in one variable necessarily causes the change in the other.
The relationship between two sets of data, that when one changes, the other is likely to make a corresponding change. If the changes are in the same direction, then there is a positive correlation. If it is in the opposite direction, then it is a negative correlation.
the extent to which two or more things are related to one another. The degree to which things are related to one another (correlated) is typically expressed in a number called the correlation coefficient (which ranges from -1.0 to +1.0). See positive correlation, negative correlation.
the degree to which two entities have similar variation. If the correlation between two populations is 1, they are perfectly correlated and both will increase or decrease similarly. Zero correlation means that the two entities vary independently of one another.
A statistical relationship between two variables such that high scores on one factor tend to go with high scores on the other factor (positive correlation) or that high scores on one factor go with low scores on the other factor (negative correlation).
Between two random Variables, the correlation is a measure of the extent to which a change in one tends to correspond to a change in the other. One measure of linear Dependence is the Correlation coefficient p. If Variables are independent random Variables then p = 0. Values of +1 and -1 correspond to full positive and negative Dependence respectively. Note: the existence of some correlation need ...
Correlation is a measure of the relation between two or more variables. Correlation coefficients can range from -1.00 to +1.00. The value of -1.00 represents a perfect negative while a value of +1.00 represents a perfect positive correlation. A value of 0.00 represents a lack of correlation.
A standardized statistical measure of the degree to which two variables move together over time. Adding an asset with low returns correlation relative to returns of other investments already in the portfolio yields risk-lowering diversification benefits. For example, the SciVest Market Neutral Equity Fund, with near zero correlation to the major market indices, is thereby delivering a "smoother ride" and a dramatically decreased risk when added to a portfolio.
Quantifies the extent to which two variables are related to each other. it is measured in the range of +1 to -1. A correlation of +1 indicates a perfect positive relationship, ie. as one goes up, the other goes up by the same amount. A correlation of -1 indicates a perfect negative relationship, ie. as one goes up, the other goes down by the same amount. A correlation of 0 indicates that the two variables are completely independent of each other. See also Linefitting.
A statistical measure referring to the relationship between two or more variables (events, occurrences etc.). A correlation between two variables suggests some causal relationship between these variables. Typically the Swiss Franc is closely correlated with the German Mark.
A statistical measure referring to the relationship between two or more variables (events, occurrences etc). A correlation between two variables suggests a relationship between these variables. Typically the New Zealand Dollar is closely correlated with the Australian Dollar.
Correlation is a measure of the strength of the relationship between two variables. Correlation doesn't guarantee a cause and effect relationship between two variables but is a necessary condition to such a relationship.
The tendency of individual securities to move together. A correlation coefficient of +1 means that two securities will always move up and down together. A correlation coefficient of -1 means that one security moves down when another moves up. A correlation coefficient of zero means that the movement of the two securities shows no pattern.
A causal, complementary, parallel, or reciprocal relationship, especially a structural, functional, or qualitative correspondence between two comparable entities. The simultaneous change in value of two numerically valued random variables.
the extent to which two securities â€œmove togetherâ€ or â€œin tandemâ€; eg., for many time periods, stock returns are positive while bond returns are negative and they are said to be â€œnegatively correlatedâ€; equity style returns tend to move together or are positively correlated but at different rates
A measure of how two traits vary together. A correlation of +1.00 means that as one trait increases the other also increases -- a perfect positive relationship. A correlation of -1.00 means that as one trait increases the other decreases -- a perfect negative, or inverse, relationship. A correlation of 0.00 means that as one trait increases, the other may increase or decrease -- no consistent relationship. Correlation coefficients may vary between +1.00 and -1.00.
Correlation is a statistic used to measure the strength and direction of the association between two sets of scores. (coefficients range from +1 00 to -1 00. A correlation of +1 .00 indicates a perfect positive relationship between the scores, a correlation of 00 indicates no relationship between the scores, and a correlation of-1.00 indicates an inverse relationship
Correlation is a measure of the degree of relationship or association between two variables. A value close to +1 indicates a strong positive correlation, a value close to -1 indicates a strong negative correlation, while a value close to 0 indicates a weak or no correlation.
A correlation is simply the relationship between two things which can be expressed numerically. If the two things are exactly the same then the relationship is perfect = +1.0. If there is no relationship between them then = 0.0. If the two things are exact opposites then = -1.0. Within the workplace correlations are useful for example to see whether a test predicts later job performance.
When correlating two variables we measure the strength of the relationship between them. The correlation coefficient is in the range â€“1 to +1, with the absolute value indicating the strength. A negative coefficient indicates an inverse relationship (i.e. as one goes up the other goes down), 0 indicates no relationship and a positive coefficient indicates a positive relationship. In CSM we would only expect to find positive coefficients. The most common type of correlation is Pearson's r.
the Pearson product moment correlation coefficient. The correlation between variables 1 and 2 is denoted by 12. The defining formula for 12 is where Z i1 and Z i2 are the z-scores for case i on variables 1 and 2, and N is the sample size. In factor analysis we assume the correlation between two variables is due to their mutual relationships with common factors. See c ommon factor analysis and the fundamental theorem of factor analysis .
The degree to which economic variables are observed to move together: If they move in the same direction, there is positive correlation; if they move in opposite directions, there is negative correlation.
Correlation describes how a fund and its benchmark have historically moved in relation to each other. Correlation coefficients (measure of the relative movement) range from â€“1.0 through to +1.0. A correlation coefficient of +1.0 implies that a fund consistently moves in the same direction as its benchmark and a correlation coefficient of â€“1.0 may be interpreted as meaning a fund consistently moves in the opposite direction to its benchmark. A correlation coefficient of zero suggests that a fund and its benchmark are not correlated.
A linear statistical measure of the comovement between two random variables. A correlation (Greek letter "r", pronounced "rho") will range from +1.0 to -1.0. For market risk, international equity markets rising and falling together is an example of positive correlation. In credit risk, "clumps" of firms defaulting together by industry or geographically is an example of positive correlation of default events.
The extent to which one observation or computed value is influenced by the change in an other, or that both are influenced by a third. (e.g. The error in a trigonometric height difference is correlated to the error in both the vertical angle and the measured distance. Similarly errors in computed GPS vectors have correlations between their individual components (X, Y, Z) and between common phase observations.) The correlation coefficient is the proportion of the total variation in the dependent variable (y) which can be attributed to the relationship with the independent variable (x).
refers to a statistical measure of relationship. This statistic, sometimes referred to as rho, can vary from -1 through 0 to +1. Positive numbers indicate that two variable or measures tend to increase of decrease together, i.e. as one increases so does the other. Negative numbers indicate that two measures tend to move in opposite directions, i.e. as one increases the other one decreases. Correlations near 0 indicate that two measures are not related. Evaluating when a correlation is significant (sufficiently distant from 0) requires specific calculations related to each analysis.
Correlation describes the relationship between two different variables. If one increases when the other increases then there is a correlation between them. For example there is a correlation between the speed at which a car travels and the risk of death in an accident. The fact that there is a correlation does not necessarily mean that one causes the other. They could both be due to some third common factor.
(go to top) A statistical measure of the degree to which the movements of two variables are related. A correlation of 1 between two different asset classes means that they have moved completely in line with each other. A correlation of â€“1 between two asset classes means that they have moved completely in opposition to each other. A correlation of zero means that the asset classes show no relationship in the way that they move relative to each other.
1) A common statistical analysis, usually abbreviated as , that measures the degree of relationship between pairs of interval variables in a sample. The range of correlation is from -1.00 to zero to +1.00. 2) A non-cause and effect relationship between two variables.
A method of establishing age relationships between various rock strata. There are two basic types of correlation: physical correlation, which requires comparison of the physical characteristics of the strata, and fossil correlation, the comparison of fossil types.
(time series, autocorrelation, serial, and spatial) is the linear statistical relationship between two random variables. The correlation that describes the relationship (1) in time, is serial correlation or lagged correlation (see also autocorrelation), (2) in space is spatial correlation, and (3) between different climate variables is the cross-correlation. [pg 4-6, 2
A measure of the extent to which two variables tend to be associated, vary, or occur together. Correlation can be positive or negative and is scaled to lie between -1 and +1. If two variables are positively correlated, their values tend to increase and/or decrease together. If two samples are negatively correlated, the values of one increase while the values of the other decrease. In addition, correlation can be linear or non-linear. If two samples are linearly correlated, there values tend to fall along a straight line when plotted against one another. If two samples are nonlinearly correlated, their values tend to follow a nonlinear pattern.
Used to describe the observed relationship between instances of two events. A systematic pattern can be seen in the occurrences of events that are correlated. When the events involve numbers, a positive correlation means that as one increases, the other increases as well. A negative correlation means that as one increases, the other decreases. Correlation does NOT imply causation in any way. In other words, just because two events are correlated does not mean that one causes another, or has anything to do with the other - correlations deal only with observed instances of events, and any further conclusions cannot be inferred from correlation alone. Strong correlation, however, does often warrant further investigation to determine causation.
Between â€“1 and 1, the correlation expresses the power of the relation between the fund's performance and that of the benchmark. A correlation of 1 means that the fund is behaving in exactly the same fashion as its benchmark: the two assets are performing in the same way (positive) and in identical proportions. The value of the correlation reflects the more or less close link between two assets.- greater than 0.70: strong link between two assets,- between 0.40 and 0.69: weak link,- less than 0.30: no real relation,- close to 0 either positive or negative: total absence of relation. A negative correlation between two assets means that when an asset moves in one direction, the other asset moves in the opposite one. To diversify a portfolio and therefore reduce the risk without necessarily changing its performance, it is useful to have weakly correlated assets.
A measurement of relationship between two variables. The correlation coefficient (r) shows if there is any correlation between an asset and the market. 1.0 is perfect correlation, 0.0 is absolutely no correlation, and -1.0 is a perfect negative correlation. Studies indicate that a correlation coefficient below 0.3 has no correlation to the market.
Determination of the position of stratigraphically equivalent rock units in different wells, often done by matching the acter of geophysical logs; also the matching of variables, such as log response and core analyses.
Statistical measure of the degree to which the movements of two variables are related. A correlation of 1 between two different shares means that they have moved completely in line with each other (albeit at perhaps a different magnitude). A correlation of -1 between two shares means that they have moved completely in opposition to each other. A correlation of 0 means that the shares show no relationship in the way that they move relative to each other.
The relationship between two variables, for example economic growth and the performance of certain market segments. The more one variable behaves in the same way as the second, the higher the correlation.
the degree of systematic linear relationship between two variables. A positive correlation implies that when one variable is above its mean, the other one also tends to be; and likewise for both tending to be below their means. A negative correlation implies that when one variable is above its mean, the other one tends to be below its mean, and vice versa. A coefficient of linear correlation ranges from -1 for a perfect negative correlation, through zero for no relationship, to +1 for a perfect positive correlation. "Among adults, height and weight are positively correlated to a moderately high degree."
Correlation measures the degree to which two investments tend to move in step. This number is always between -1 and 1 (-100% to 100%). A correlation of 1 indicates that the investments behave identically; a correlation of -1 indicates that one investment always has an exactly opposite move to the other.
The extent to which two quantities â€“ e.g. test scores â€“ are connected in individuals, i.e. the tendency for children who are good at one thing to be good at another, and vice-versa. For instance, verbal and non-verbal reasoning scores are strongly correlated in children. As are height and weight. Correlation is very important in psychology and in testing because if scores correlate well, it suggests that there is something in the tests that taps into similar parts of the mind. There are many ways of approaching correlation in statistics, and many indicators of it. Most use a scale of â€“1 to +1, the former indicating perfect disagreement, the latter perfect agreement, with all practical examples falling somewhere between. A value of zero means that the quantities in question are not connected at all. This happens surprisingly rarely.
Also known as Reconciliation, with regard to an appraisal; it is the bringing together of the values determined by the three approaches to value (cost, market and income) into mutual relationship with each other.
Measures the degree to which two variables (such as a fund and its benchmark) move together. A correlation coefficient varies from -1.0 to 1.0. -1.0 indicates perfect negative correlation and +1.0 indicates perfect positive correlation.
the method by which rocks units or strata are compared and time-relationships between them are established. This can be done by examining the rock type and succession, the fossil content, or by chemical analysis.
(1) The equivalence in stratigraphic positions of formations in different wells. Similarities in the character of well-logging responses and the occurrence of distinctive features which serve as markers from one well to the next are used.
A technique for definining unique properties for a message known as a correlation type, used to associate it with a given instance of an orchestration, and the message's proper sequence as defined by a correlation set.
direct correlation The Ornstein-Zernike equation and indirect correlation The Ornstein-Zernike equation and intermolecular correlation Molecular liquids intramolecular correlation Molecular liquids | Molecular liquids total correlation The Ornstein-Zernike equation and
A key quality control parameter, this is essentially a measurement of how much the particle distribution has changed between phase measurements. The less the distribution has changed, the higher the correlation, and the more precise the velocity measurement.
A mathematical measure of the similarity between images or areas within an image. Pattern matching, or correlation of an X by Y array size template to the same size image, produces a scalar number, the percentage of match. The template is usualy walked through a larger array to find the highest match.
This is a process that occurs when using spread spectrum techniques and it is the process of synchronizing the phase of a local PN sequence within a radio receiver with that of the incoming spread signal in order to “despread” and recover the narrowband data signal.
The selections that the buyer makes for a new home. All plumbing fixtures, door knobs, flooring and sink/tub choices are all examples of items that need to be correlated. A home can not be built without all correlations complete. All correlations are made with our design consultant then presented in a document form for the homeowner to authorize final approval.
The final step in the appraisal process where the appraiser considers three estimates of value derived from cost, income and market data approaches. The correlation process weighs the influence of each method in relation to the specific property type and final estimate of value.