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The  scatterplot  shows  a  positive  relationship  between  the  two  variables—studying  increases  (marks)  percentage.
              However,
              •  there is a student who studies 3 hours but still achieves a low percentage which is called outlier.
              •  outliers are data points that do not follow the logic of the dataset.


                      Regression—Finding the Line

              When we make a distribution in which there is an involvement of more than one variable, then such an analysis is called
              Regression Analysis. Regression generally focuses on predicting the value of the variable that is dependent on the other.
              Let us consider two variables x and y.

              y – Regression or Dependent Variable
              x – Independent Variable or Predictor
              Therefore, if we use a simple linear regression                           line: y=mx+b
              model where y depends on x, then the regression
              line of y on x is:                                              y (dependent)   variable

                                                              y = mx + b
              where,                                                                     ŷ 2
                                                                              y
              •  x is the independent variable.                                1         y 2

              •  y is the dependent variable.
              •  m is the slope of the line.                         y-intercept    x
              •  b is the y-intercept.                                               1      x (independent) variable


              Least Squares Method—Finding the Line of Best Fit
              Consider the following example where marks of 10 students are shown which they scored after a certain number of
              hours of study:

                                                   No. of Hours Studied    Marks

                                                             2              44
                                                             9              98

                                                             5              80

                                                             3              75

                                                             7              70

                                                             1              63
                                                             8              53

                                                             6              92

                                                           2.5              71

                                                             4              65






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