Tuesday, April 25, 2017

37. PEARSON CORRELATION

OBJECTIVE

Find out which quantitative variables are related to each other and define the degree of correlation between pairs of variables.


DESCRIPTION

This method estimates the Pearson correlation coefficient, which quantifies the strength and direction of the linear association of two variables. It is useful when we have several variables that may be correlated with each other and we want to select the ones with the strongest relationship. Correlation can be performed to choose the variables for a predictive linear regression.

Pearson Correlation

Correlation Matrix

With the Excel Data Analysis complement, we can perform a correlation analysis resulting in a double-entry table with correlation coefficients (Pearson’s coefficients). We can also calculate correlations using the Excel formula “=CORREL().” The sign of the coefficient (Pearson correlation coefficient) represents the direction (if x increases then y increases = positive correlation; if x increases then y decreases = negative correlation), while the absolute value from 0 to 1 represents the strength of the correlation. Usually above 0.8 it is very strong, from 0.6 to 0.8 it is strong, and when it is lower than 0.4 there is no correlation (or it is very weak).

The figure above shows that there is a very strong positive correlation between X1 and Y and a strong positive correlation between X1–X3 and X3–Y. X3 and X4 have a weak negative correlation.



TEMPLATE


No comments:

Post a Comment