1, linear regression equation is one of the statistical analysis methods to determine the 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.
2. Regression linear equation refers to the straight line that can best reflect the relationship between X and Y in a set of data (X and Y) with related variables.
As the difference between the ordinate y of the regression line corresponding to Xi and the observed value Yi, the geometric meaning of the deviation can be described by the distance between the point and its projection in the vertical direction of the regression line. Mathematical expression: yi-y = yi-a-bxi.
The total deviation cannot be expressed by the sum of n deviations, but is usually calculated by the sum of squares of deviations, that is, (yi-a-bxi) 2.
Linear regression has many practical uses. Divided into the following two categories:
1. If the target is prediction or mapping, linear regression can be used to fit the prediction model to the value of the sum x of the observed data set. When this model is completed, for the newly added X value, the fitted model can be used to predict the Y value without giving a matching Y value. ..
2. given a variable y and some variables x1->; Linear regression analysis can be used to quantify the correlation strength between Y and Xj, evaluate Xj that is not related to Y, and identify which subsets of Xj contain redundant information about Y. ..