Pearson product-moment correlation coefficient
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Dina matematik, sarta dina sabagean statistik, the Pearson product-moment correlation coefficient (r) is a measure of how well a linear equation describes the relation between two variables X and Y measured on the same object or organism. It is defined as the sum of the products of the skor standard of the two measures divided by the degrees of freedom:
The result obtained is equivalent to dividing the covariance between the two variables by the product of their standard deviations. In general the quantity of a correlation coefficient is the square root of the coefficient of determination (r2), which is the ratio of explained variation to total variation:
- Y = a score on a random variable Y
- Y' = corresponding predicted value of Y, given the correlation of X and Y and the value of X
- = mean of Y
The correlation coefficient adds a sign to show the direction of the relationship. The formula for the Pearson coefficient conforms to this definition, and applies when the relationship is linear.
The coefficient ranges from -1 to 1. A value of 1 shows that a linear equation describes the relationship perfectly and positively, with all data points lying on the same line and with Y increasing with X. A score of -1 shows that all data points lie on a single line but that Y increases as X decreases. A value of 0 shows that a linear model is inappropriate – that there is no linear relationship between the variables.
The linear equation that best describes the relationship between X and Y can be found by linear regression. If X and Y are both normally distributed, this can be used to "predict" the value of one measurement from knowledge of the other. That is, for each value of X the equation calculates a value which is the best estimate of the values of Y corresponding the specific value of X. We denote this predicted variable by Y.
Any value of Y can therefore be defined as the sum of Y and the difference between Y and Y:
The varian of Y is equal to the sum of the variance of the two components of Y:
Since the coefficient of determination implies that sy.x2 = sy2(1 − r2) we can derive the identity
The square of r is conventionally used as a measure of the strength of the association between X and Y. For example, if the coefficient is .90, then 81% of the variance of Y is said to be explained by the changes in X and the linear relation between X and Y.
r is a statistik parametrik. It assumes that the variables being assessed are normally distributed. If this assumption is violated, a non-parametric alternative such as Spearman's ρ may be more successful in detecting a linear relationship.