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The correlation coefficient is +1 in the case of a perfect direct (increasing) linear relationship (correlation), −1 in the case of a perfect inverse (decreasing) linear relationship (anti-correlation), [5] and some value in the open interval (,) in all other cases, indicating the degree of linear dependence between the variables. As it ...
corr – correlation. cos – cosine function. cosec – cosecant function. (Also written as csc.) cosech – hyperbolic cosecant function. (Also written as csch.) cosh – hyperbolic cosine function. cosiv – coversine function. (Also written as cover, covers, cvs.) cot – cotangent function. (Also written as ctg.) coth – hyperbolic ...
A correlation coefficient is a numerical measure of some type of linear correlation, meaning a statistical relationship between two variables. [ a ] The variables may be two columns of a given data set of observations, often called a sample , or two components of a multivariate random variable with a known distribution .
3. Subfactorial: if n is a positive integer, !n is the number of derangements of a set of n elements, and is read as "the subfactorial of n". * Many different uses in mathematics; see Asterisk § Mathematics. | 1. Divisibility: if m and n are two integers, means that m divides n evenly. 2.
Given an independent variable x and a dependent variable y, y is directly proportional to x [1] if there is a positive constant k such that: y = k x {\displaystyle y=kx} The relation is often denoted using the symbols "∝" (not to be confused with the Greek letter alpha ) or "~", with exception of Japanese texts, where "~" is reserved for ...
With any number of random variables in excess of 1, the variables can be stacked into a random vector whose i th element is the i th random variable. Then the variances and covariances can be placed in a covariance matrix, in which the (i, j) element is the covariance between the i th random variable and the j th one.
The following table lists many common symbols, together with their name, how they should be read out loud, and the related field of mathematics. Additionally, the subsequent columns contains an informal explanation, a short example, the Unicode location, the name for use in HTML documents, [ 1 ] and the LaTeX symbol.
Pearson's correlation coefficient is the covariance of the two variables divided by the product of their standard deviations. The form of the definition involves a "product moment", that is, the mean (the first moment about the origin) of the product of the mean-adjusted random variables; hence the modifier product-moment in the name.