How to derive the regression equation in excel

1. First of all, enter the data to be processed in Excel**, like the figure above;

2. Then use the mouse to select the selected data area, and select Insert at the top of the toolbar;

3. Select Insert**, then select this Chang chart type as a scatter chart, and select the first type for the graph;

4. Click OK to see the generated **, and now you can do some basic beautification of the appearance of the wanton type**;

5. Then, click on any small dot on the table, you will find that these small dots have become a star, and then click the right button, a selection box will pop up;

6. Then select the crack and guess to add the trend line item, and a regression curve will be generated, which is to continue to right-click**, see the setting**, click in, select linear, display equation, r squared, and it is done.

Note: Remember to select the dots in the table to add trend lines.

Excel gets the regression equation and knows that the idea of finding y is as follows: put the y value in a column, such as column a, and enter the solution formula of x in column b. If the regression equation is y=5x2(square)+2, enter =sqrt((a1-2)5) in b2.

Then drag the B2 unit to align with column A. Or double-click on the small black square megabeam in the lower right corner of unit B2. Return to the good guess Li equation friend late:

It is a mathematical expression that reflects the regression relationship between one variable (dependent variable) to another variable or group of variables (independent variable) obtained through regression analysis based on sample data. The regression linear equation is used a lot, and the least squares method can be used to find the regression linear equation a,b, so as to obtain the regression linear equation.

1. Click on Excel.

2. Click File - Options in the upper left corner to pop up a dialog box.

3. Click Add-ons on the left, then click Go at the bottom of the middle.

4. Click the hook in all the options in front of the pop-up box, and then OK.

5. Click on the data in the upper left, and then the data analysis will appear at the end of it.

6. Click in to select the regression point to determine.

7. Check your x,y area, click on the residual diagram in the residuals, and it's OK.

Find the average of x,y first.

x =(3+4+5+6) 4=9 2,y =(, then find the sum of the products of the corresponding x and y : 3* ,x *y =63 4 , and then calculate the sum of the squares of x: 9+16+25+36=86,x 2=81 4, and now you can calculate b:

b=(86-4*81 4)=, while a=y-bx =7, so regress to talk about the trace equation.

is y=bx+a= .

A regression straight line equation is a straight line that best reflects the relationship between x and y between the data (x and y) of a set of variables that have a correlation.

The geometric significance of the dispersion is the difference between the ordinate y of the regression line corresponding to xi and the observed value yi, and its geometric significance can be described by the distance between the point and its projection in the vertical direction of the regression line. Mathematically expressed: yi-y = yi-a-bxi.

The total dispersion cannot be expressed as the sum of n dispersions, and the large rent is usually calculated as the sum of the squares of the dispersions, i.e. (yi-a-bxi) 2.

To determine the regression linear equation, it is only necessary to determine a and the regression roller coefficient b. The method of finding the regression line is usually the least squares method: the dispersion is the difference between the ordinate y of the regression line corresponding to xi and the observed value yi, and its geometric significance can be described by the distance between the point and its projection in the vertical direction of the regression line. Travel.