linear regression equation example equation

Now if you put a value of that was not used in the calculation you will get the value of y predicted which will line on the line like the red dot.

Here is a visual representation of how the regression line will look like finally. The Best Fit Line in matplotlib | Image by author

That is, we subtract the height of the line at that point xᵢ, that is Yᵢ from the real height of the point yᵢ and assume the value is eᵢ From the figure below let us assume that the blue line is the regression line and then we will try to find the perpendicular distance of the line from that point marked as red present at (xᵢ, yᵢ) on the graph. Now let us learn how to find mse so that you can calculate on your own. The lower the value of the mean squared error is the better the line should be.Īs I said earlier squared error is the function to know how much error is present in the line with respect to the plotted data, the better the line is the minimum is the value of mse. For now, think of it as a function that tells how good the line is working. To know why this is true you and I have to go in-depth with mean squared error ( ooo another buzzword! ). The approach of prediction is true all right but not 100% guaranteed and that’s why in my school book, at the end of the definition there is written: “on an average”. Various lines in a matplotlib scatterplot | Image by author

Linear regression equation example equation