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Help with Correlation Coefficient Analysis

What Is a Correlation?

A correlation is a statistical concept that explains the relationship between two variables. Remember that the variables are the unknown numbers like x, y, and n that you’re trying to solve for. Correlation coefficients range from -1 to +1, meaning some correlations are negative and others are positive. The correlation explains how X affects Y, or vice versa.

statistical data analysis schema

Correlation Coefficient Analysis

The negative coefficient indicates a negative correlation, like this: The more games a football player loses, the less likely he is to be picked up by the best team in the league next season. The positive coefficient indicates a positive correlation, like this one: the more time a super hero spends fighting crime, the more injuries he sustains. Also, the number of the coefficient (from 0.001 to 1.00) explains the strength of the relationship; whether X explains 44% of Y, or 82%, or even 100% of Y.

Example: The more hours Pablo spends drinking at the pub (X) , the lower his test scores (Y) become. That means there is an inverse relationship between his drinking time and his success in school. If the correlation coefficient is -0.56, that means his drinking time negatively affects his academic performance, but the pub explains only 56% of why he is not doing well in school.

Canonical Correlation Analysis R

R is a standard variable used in statistics. Canonical correlation analysis comes into play when there are multiple variables that all supposedly affect each other. Instead of having just X and Y, or just X, Y, and Z, we would have groups like (X1, Xn) and (Y1, Yn) and (Z1, Zn). Canonical would try to explain the relationship between variables most likely to affect each other, such as how X1 affects Y1 and Z1, or how Xn and Yn affect Zn. Advanced classes will study linear and nonlinear canonical correlation analysis.

Need Help with a Correlation Statistical Test?

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