Conceptually it's basically OK.

In applied disciplines, analyzing correlation coefficients is a very common practice.

For example: Many financial analysts judge the relationship between two stock prices (actually two time series) by correlating them. This practice is very common in the industry, such as fund managers.

Analyze the correlation coefficients between the stocks in his portfolio to achieve the purpose of maximizing returns (portfolio expected value) while minimizing risk (portfolio standard deviation).

For example, stock price fluctuations in the same sector (such as the high-tech sector) are often positively correlated.

Stock price fluctuations between directly competing industries or companies are often negatively correlated.

The following is copied from the entry: The correlation coefficient is also called the linear correlation coefficient. It is an indicator that measures the degree of linear correlation between variables.

The sample correlation coefficient is represented by r, the overall correlation coefficient is represented by ρ, and the value range of the correlation coefficient is.

The larger the value of |r|, the smaller the error Q, and the higher the degree of linear correlation between variables; the closer the value of |r| is to 0, the larger the value of Q, and the lower the degree of linear correlation between variables.

The correlation coefficient, also known as the Pioneer product-moment correlation coefficient, is a statistical analysis index that illustrates the closeness of the correlation between two phenomena.

The correlation coefficient is represented by the Greek letter γ, and the γ value ranges between -1 and +1.

γ>0 is a positive correlation, and γ<0 is a negative correlation.

γ=0 means no correlation; the larger the absolute value of γ, the higher the degree of correlation.

The degree of correlation between two phenomena is generally divided into four levels: if there is a positive correlation between the two, r will have a positive value, and when r=1, it is a completely positive correlation; if there is a negative correlation between the two, r will have a negative value, and r=

When -1, it is a completely negative correlation.

When there is a perfect positive or negative correlation, all graph points are on the linear regression line; the more discrete the points are distributed above and below the linear regression line, the smaller the absolute value of r.

When the number of examples is equal, the closer the absolute value of the correlation coefficient is to 1, the closer the correlation; the closer it is to 0, the less close the correlation.

When r=0, it means that there is no linear relationship between the two variables X and Y.

Usually when |r| is greater than 0.8, the two variables are considered to have a strong linear correlation.