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Video Session


                     Scan the QR code or visit the following link to watch the video: Linear Regression Algorithm
                     https://www.youtube.com/watch?v=E5RjzSK0fvY&t=527s







                             Brainy Fact


                    The least squares regression method was first published by mathematicians Legendre in 1805 and Carl
                    Friedrich Gauss in 1809. Both used linear regression to predict the movement of planets around the sun.
                    Gauss later published an improved method in 1821.





                      Regression—Describing the Line

              In statistics, the regression line is the line that best describes the behaviour of the dataset. The regression line is very
              useful for forecasting purposes. Using the equation obtained from the regression line, the analyst can predict the future
              behaviour of the dependent variable by entering different values for the independent variable.



                                                                   y = mx + b



                                                                                 slope ≡ m
                                                     y




                                             y - int ≡ b

                                                                        x



              Interpreting the Slope of a Regression Line
                                                                                                         2
              The slope is interpreted in algebra as ‘rise over run’. For example, if the slope is 2, you can write this as    and say that
                                                                                                         1
              as you move along the line, as the value of the variable X increases by 1, the value of the variable Y increases by 2. In the
              context of regression, the slope tells you how much Y will vary as X increases.


              Interpreting the y-intercept of a Regression Line
              The y-intercept is the place where the regression line y = mx + b intersects the y-axis (where x = 0), and is denoted by b.
              Sometimes the y-intercept can be interpreted in a meaningful way, and sometimes not. This is different from slope, which
              is always interpretable.

              Let us understand the above using some examples:




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