Descriptive Statistics -- Correlation Coefficient
 
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Correlation Coefficient

The correlation coefficient, also called the cross-correlation coefficient, is a measure of the strength of the relationship between pairs of variables. Origin provides both parametric and non-parametric measures of correlation.

  • Pearson's r Correlation

  • Spearman's Rank Order Correlation

  • Kendall's tau Correlation
An Origin report sheet of correlation coefficient analysis, shown with a scatter matrix plot of the variables.
The Descriptive Stats: corrcoef dialog box used for configuring correlation coefficient analysis.
The Correlation Coefficients report sheet with expandable branches

Choosing a Correlation Test

  • Pearson's r Correlation
    This widely-used coefficient measures the strength of a linear association between variables.

  • Spearman's Rank Order Correlation
    The most common non-parametric measure, Spearman's is used when data are not normally distributed. Spearman's is a non-parametric equivalent of Pearson's correlation.

  • Kendall's tau Correlation
    Another non-parametric method, used when analyzing data with one or more ordinal variables. Kendall’s is relatively "robust" to outliers.

Range of Observed Values

The correlation coefficient can range in value from +1 to -1. When the coefficient is positive, an increase in the value of one variable will be accompanied by an increase in the value of the compared variable. A negative correlation coefficient indicates an inverse relationship between the two variables.

Strength of Association

The closer the correlation coefficient is to +1 or -1, the stronger the association of the two variables. When reporting results, Origin provides the option to flag significant correlations, giving you quick insight into your data.

Plotting a Scatter Matrix with Correlation Coefficients

You can plot a scatter matrix and label plots with correlation coefficient values. For information, read about the scatter matrix plot feature.

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