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This article shows you how to carry out the correlation analysis and test of R language in big data. The content is concise and easy to understand, which can definitely brighten your eyes. I hope you can get something through the detailed introduction of this article.

The correlation coefficient can be used to describe the relationship between quantitative variables. The positive and negative signs of the results indicate positive or negative correlation respectively, and the magnitude of the value indicates the strength of the correlation.

R can calculate a variety of correlation coefficients. Today, we mainly introduce three common correlation coefficients: Pearson correlation coefficient, Spearman correlation coefficient and Kendall correlation coefficient. These three correlation coefficients can be calculated by cor function in R language and specified by method function.

One correlation analysis

1.1 Pearson correlation coefficient

To measure the linear correlation between two continuous variables, the standard deviation of both variables is not zero. In addition, the applicable conditions of Pearson correlation coefficient are as follows:

The main results are as follows: 1) the relationship between variables is linear and all are continuous data.

2) the variables are generally normal distribution or close to normal distribution.

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