In statistics, when linear regression is used to analyze variables and least square method is used to estimate parameters, R square is the ratio of the sum of squares of regression to the sum of squares of total deviation, indicating the ratio of the sum of squares of total deviation that can be explained by the sum of squares of regression. The more accurate the model is, the more significant the regression effect is. R squared is between 0 and 1, and the closer it is to 1, the better the regression fitting effect is. Generally speaking, the goodness of fit of models exceeding 0.8 is higher.
Linear regression analysis
It is a method to predict the future value of a random variable related to it according to the change of one or a group of independent variables. Regression analysis needs to establish a regression equation that describes the correlation between variables. According to the number of independent variables, the regression equation can be univariate regression or multivariate regression. If the regression function is linear, the variables are said to be linearly correlated. One-dimensional linear regression analysis includes two variables, one is an independent variable, represented by X, and the other is a dependent variable (predictive variable), represented by Y.