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Chi Square

Q: What particular measure of correlation would be most appropriate for use with two variables measured at an ordinal level?

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Either an Interval or an Ordinal Scale

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.

The correlation between an asset's real rate of return and its risk (as measured by its standard deviation) is usually:

Nominal Variables

No. Correlation coefficient is measured from +1 to -1. In addition, if the two sets of exam are exactly same, their correlation coefficient is +1.

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Chi Square

Yes, correlations can be measured using statistical methods such as Pearson's correlation coefficient or Spearman's rank correlation coefficient. These measures quantify the strength and direction of the relationship between two variables.

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 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.

Regression coefficient measures the change in the dependent variable for a one-unit change in the independent variable, while correlation coefficient measures the strength and direction of the linear relationship between two variables. Regression coefficient is specific to the relationship between two variables in a regression model, while correlation coefficient is a general measure of association between two variables.

dependent variables

dependent variables

dichotomous variables

Variables measured in monetary units