The graph of stationary R square and R square means that it is the square of R, and R square is usually used to describe the fitting degree of data to the model. Generally speaking, R-square and adjust R-square are commonly used.

r？ It refers to the goodness of fit, that is, the fitting degree of the regression line to the observed value. Expression: R2=SSR/SST= 1-SSE/SST, where: SST=SSR+SSE, SST (sum of total squares) is the sum of total squares, SSR (sum of regression squares) is the sum of regression squares, and SSE (sum of error squares) is the sum of residual squares.

R plaza

(r square) reflects the influence of the change of performance benchmark on the performance of the fund, and the influence degree is 0 ~ 100. If the R-squared value is equal to 100, it shows that the change of fund income is completely caused by the change of performance benchmark; If the R-squared value is equal to 35, 35% of the fund's income can be attributed to the change of performance benchmark. In short, the lower the square value of R, the smaller the change of fund performance caused by the change of performance benchmark.