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.
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
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