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Unit 9: Correlation and Regression




                                                                                                Notes
                                            Figure  9.1


                   Y                                  Y








                   O                     X             O                     X
                    Positive Linear Relationship        Negative Linear Relationship
                   Y                                  Y









                   O                     X            O                      X
                      Non-Linear Relationship                  No Relation



          If all the points or dots lie exactly on a straight line or a curve, the association between the
          variables is said to be perfect. This is shown below:

                                            Figure  9.2

            Y                        Y                       Y









            O                      X O                     X O                     X
                  Perfect Positive         Perfect Negative       Perfect Non-Linear
                 Linear Relationship      Linear Relationship     Linear Relationship

          A scatter diagram of the data helps in having a visual idea about the  nature of association
          between two variables. If the points cluster along a straight line, the association between variables
          is linear. Further, if the points cluster along a curve, the corresponding association is non-linear
          or curvilinear. Finally, if the points neither cluster along a straight line nor along a curve, there
          is absence of any association between the variables.
          It is also obvious from the above figure that when low (high) values of X are associated with low
          (high) value of Y, the association between them is said to be positive. Contrary to this, when low
          (high) values of X are associated with high (low) values of Y, the association between them is
          said to be negative.









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