Either an Interval or an Ordinal Scale
The Correlation Coefficient computed from the sample data measures the strength and direction of a linear relationship between two variables. The symbol for the sample correlation coefficient is r. The symbol for the population correlation is p (Greek letter rho).
A correlation of 0.20 is somewhat low, meaning that the degree of linear relationship measured between the two variables involved is low. However, such a degree of relationship would not be ignored in many fields of science where relationships are difficult to detect. Correlation is rarely if ever put in terms of percentage.
See related link. As stated in the link: In probability theory and statistics, correlation (often measured as a correlation coefficient) indicates the strength and direction of a linear relationship between two random variables
A correlation can be measured by comparing negative and positive aspects of two or more items. If there are 4 items and 4 identical positives there is a 100% correlation between the 4 items.
A correlation function is the correlation between random variables at two different points in space or time, usually as a function of the spatial or temporal distance between the points. If one considers the correlation function between random variables representing the same quantity measured at two different points then this is often referred to as an autocorrelation function being made up of autocorrelations. Correlation functions of different random variables are sometimes called cross correlation functions to emphasise that different variables are being considered and because they are made up of cross correlations.Correlation functions are a useful indicator of dependencies as a function of distance in time or space, and they can be used to assess the distance required between sample points for the values to be effectively uncorrelated. In addition, they can form the basis of rules for interpolating values at points for which there are observations.Correlation functions used in astronomy, financial analysis, and statistical mechanics differ only in the particular stochastic processes they are applied to. In quantum field theory there are correlation functions over quantum distributions.
difference between correlation and regression?(1) The correlation answers the STRENGTH of linear association between paired variables, say X and Y. On the other hand, the regression tells us the FORM of linear association that best predicts Y from the values of X.(2a) Correlation is calculated whenever:* both X and Y is measured in each subject and quantify how much they are linearly associated.* in particular the Pearson's product moment correlation coefficient is used when the assumption of both X and Y are sampled from normally-distributed populations are satisfied* or the Spearman's moment order correlation coefficient is used if the assumption of normality is not satisfied.* correlation is not used when the variables are manipulated, for example, in experiments.(2b) Linear regression is used whenever:* at least one of the independent variables (Xi's) is to predict the dependent variable Y. Note: Some of the Xi's are dummy variables, i.e. Xi = 0 or 1, which are used to code some nominal variables.* if one manipulates the X variable, e.g. in an experiment.(3) Linear regression are not symmetric in terms of X and Y. That is interchanging X and Y will give a different regression model (i.e. X in terms of Y) against the original Y in terms of X.On the other hand, if you interchange variables X and Y in the calculation of correlation coefficient you will get the same value of this correlation coefficient.(4) The "best" linear regression model is obtained by selecting the variables (X's) with at least strong correlation to Y, i.e. >= 0.80 or
Variables measured in monetary units