Correlation Coefficient Value

From GM-RKB
(Redirected from Correlation Coefficient)
Jump to: navigation, search

A Correlation Coefficient Value is a unit continuous statistic value that measures the strength of linear association by two continuous random variables (by a correlation function).



References

2015

Types of correlation coefficients include:
  • Pearson product-moment correlation coefficient, also known as r, R, or Pearson's r, a measure of the strength and direction of the linear relationship between two variables that is defined as the (sample) covariance of the variables divided by the product of their (sample) standard deviations.
  • Intraclass correlation, a descriptive statistic that can be used when quantitative measurements are made on units that are organized into groups; describes how strongly units in the same group resemble each other.
  • Rank correlation, the study of relationships between rankings of different variables or different rankings of the same variable

2006

  • (Dubnicka, 2006e) ⇒ Suzanne R. Dubnicka. (2006). “Random Vectors and Multivariate Distributions - Handout 5." Kansas State University, Introduction to Probability and Statistics I, STAT 510 - Fall 2006.
    • QUOTE: Suppose that X and Y are random variables with variances 2 X and 2 Y, respectively. The correlation between X and Y is given by Corr(X,Y) = X,Y = Cov(X,Y) X Y . The quantity X,Y is also called the correlation coefficient between X and Y .
  • (Starbird, 2006) ⇒ Michael Starbird. (2006). “Meaning from Data: Statistics Made Clear.” The Teaching Company
    • QUOTE: The quantification of the strength of linear association that exists between two numeric variables. The correlation coefficient takes values between -1 and 1, where negative correlations mean that as the value of one variable rises, the other falls, and positive correlations mean that the values of the two variables rise together. Values of the correlation coefficient near 1 or -1 indicate a strong linear relationship between the two variables. Values near 0 indicate no linear relationship between the two variables.