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Bivariate Correlation Analysis: When to Use It?

What Is a Correlation?

Correlation explains the relationship between variables in statistics. It explains how one variable affects another. A variable is the unknown number, such as 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. Our online academic help service can help you with bivariate correlation analysis, multivariate, nonlinear, and many other kinds of correlation analysis.

statistical data analysis schema

Bivariate Correlation Analysis

This is the statistical analysis of the correlation between only two variables, x and y. It explains how X affects Y.

Bivariate correlation analysis example: The more hours Pablo spends playing video games (X) , the lower his test scores (Y) become. That means there is an inverse relationship between his video game time and his success in school. If the correlation coefficient is -0.56, that means his video game time negatively affects his academic performance, but the video games explain only 56% of why he is not doing well in school.

Multivariate Correlation Analysis

This is the type of analysis that explains the relationship between multiple variables. In simpler terms, it explains how X affects Y and Z.  Also, it could explain how Y and Z both affect X.

Multivariate example: It was explained above that Pablo’s video game time explains 56% of his bad grades in school. A multivariate analysis would be how Pablo’s video game time (Y) and the time he spends every day with his girlfriend (Z) affect his grades in school (X).  If Y= -0.56, then Z= 0.44.

Nonlinear Canonical Correlation Analysis

This type of 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.

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