The correlation between two variables can be measured by many statistical values, and Pearson correlation coefficient is the most commonly used. For sample data, Pearson product moment correlation coefficient is defined as follows: Pearson product moment correlation coefficient of sample data (commonly known as sample correlation coefficient) is the product of standard deviation divided by sample variance * * * and sample standard deviation. The simple correlation coefficient of samples is generally expressed by r, where n is the sample size, which is the observed value and mean value of two variables respectively. R describes the degree of linear correlation between two variables. The value of r is between-1 and+1, if r >；; 0 means that two variables are positively correlated, that is, the greater the value of one variable, the greater the value of the other variable; If r < 0, it means that the two variables are negatively correlated, that is, the greater the value of one variable, the smaller the value of the other variable. The greater the absolute value of r, the stronger the correlation. It should be noted that there is no causal relationship here. If r=0, there is no linear correlation between the two variables, but there may be other ways of correlation (such as curve). Using the sample correlation coefficient to infer whether two variables in the population are related, the original hypothesis that the population correlation coefficient is 0 can be tested by t statistics. If the t test is significant, the original hypothesis that the two variables are linearly related is rejected; If the t test is not significant, the original hypothesis that two variables are not linearly correlated cannot be rejected.