The closer the square of r is to 1, the better the fitting effect and the more realistic the fitting function is. The closer the correlation coefficient is to 1, the better. Generally speaking, the requirement is greater than 0.9 and the statistical probability is generally less than 0.05 before the model can be used. In addition, the confidence interval of the residual should contain 0, but there is no strict standard to define the degree of fitting is satisfactory.

Linear regression equation is one of the statistical analysis methods to determine the interdependent quantitative relationship between two or more variables by regression analysis in mathematical statistics. Linear regression is also the first type of regression analysis that has been strictly studied and widely used in practical applications. According to the number of independent variables, it can be divided into univariate linear regression analysis equation and multivariate linear regression analysis equation.

In statistics, linear regression equation is a regression analysis that uses least square function to model the relationship between one or more independent variables and dependent variables. The function is a linear combination of one or more model parameters called regression coefficients. The case with only one independent variable is called simple regression, and the case with multiple independent variables is called multiple regression. Conversely, this should be distinguished by multiple linear regression predicted by multiple related dependent variables rather than a single scalar variable. )

In linear regression, data are modeled by linear prediction function, and unknown model parameters are also estimated by data. These models are called linear models. The most commonly used linear regression modeling is that the conditional mean of y given x value is the affine function of X.

Generally speaking, the linear regression model can be the median or other quantile of the conditional distribution of Y, assuming that X is a linear function of X. Like all forms of regression analysis, linear regression is also concerned with the conditional probability distribution of Y with a given X value, rather than the joint probability distribution of X and Y (multivariate analysis field).