The simplest is to take three points, such as: x=0, y =0, x= 1, y = 0; X =-1.y = O. Draw the X axis and the Y axis, and connect these three points with smooth curves. It depends on the direction of the curve opening. If the coefficient of the unknown quantity is positive, it will go up. Otherwise, drawing down is the easiest way.

Regression equation is a mathematical expression based on sample data, which reflects the regression relationship between one variable (dependent variable) and another or a group of variables (independent variables) through regression analysis. Regression linear equations are widely used. We can use the least square method to find A and B in the regression linear equation, and then get the regression linear equation.

If there is a set of data (X and Y) of related variables, we can observe that all data points are distributed near a straight line through the scatter plot, and we can draw many such straight lines. We hope that one of them can best reflect the relationship between X and Y, that is, we should find a straight line to make it "closest" to the known data points.

Because there are residuals in the model, which cannot be eliminated, it is impossible to determine a straight line with two points to get the equation. In order to ensure that almost all the measured values converge on a regression line, we need the minimum distance from the sum of the squares of their longitudinal distances to the best fitting line.

Let's write this linear equation (as shown on the right, written as formula ①). Here, the mark ""is added above Y to distinguish the actual value Y of Y, that is to say, when X takes the value xi = 1, 2 ..., 6), the observed value corresponding to Y is yi, and the ordinate corresponding to Xi on the straight line is called Y to X.

Regression straight line equation, the corresponding straight line is called regression straight line, and b is called regression coefficient. To determine the linear regression equation ① only need to determine a and regression coefficient B. Correlation of regression equation: E. Random variable B. Slope A. Mathematical expectation of intercept-X. Mathematical expectation of X-Y. Y.R. Accuracy of regression equation.