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Research Methodology




                    Notes          Further, using property III, we can write
                                                                                    2
                                                         Sx  2    = Sx x  = Sx (x  – b x ) = Sx  – b Sx x
                                                            i×k   i i×k  i  i  ik k  i  ik  i k
                                                              = nS -  r  S i  nS S r =  nS 2 (1 r-  2 )
                                                                  2
                                                                     ik
                                                                  i
                                                                       S   i  k ik  i  ik
                                                                        k
                                   Similarly,            Sx  2     = nS 2 (1 r-  2  ).
                                                           i×k    j    jk
                                                                    nS S r - r r  )     r -  r r
                                                                      i
                                                                       j
                                                                        ( ij
                                                                            ik jk
                                   Thus, we have           r  =                    =     ij  ik jk
                                                            ij×k    2    2  2    2 )      2     2 )
                                                                  nS i  (1 r-  ik ) ( 1nS  j  - r jk  ( 1 r-  ik )( 1 r-  jk
                                   Self Assessment
                                   Fill in the blanks:
                                   16.  In case of three variables xi, xj and xk, the partial correlation between xi and xj is defined as
                                       the simple correlation between them after eliminating the effect of………..
                                   17.  ……………… correlation is denoted as rij×k

                                   9.4 Regression Analysis


                                   If the coefficient of correlation calculated for bivariate data (X , Y ), i = 1, 2, ...... n, is reasonably
                                                                                    i  i
                                   high and a cause and effect type of relation is also believed to be existing between them, the next
                                   logical step is to obtain a functional relation between these variables. This functional relation is
                                   known as regression equation in statistics. Since the coefficient of correlation is measure of the
                                   degree of linear association of the variables, we shall discuss only linear regression equation.
                                   This does not, however, imply the non-existence of non-linear regression equations.
                                   The regression equations are useful for predicting the value of dependent variable for given
                                   value of the independent variable. As pointed out earlier, the nature of a regression equation is
                                   different from the nature of a mathematical equation,  e.g., if  Y = 10 + 2X is a  mathematical
                                   equation then it implies that Y is exactly equal to 20 when X = 5. However, if Y = 10 + 2X is a
                                   regression equation, then Y = 20 is an average value of Y when X = 5.

                                   The term  regression was first introduced by Sir Francis Galton in 1877.  In his  study of  the
                                   relationship between heights of fathers and sons, he found that tall fathers were likely to have
                                   tall sons and vice-versa. However, the mean height of sons of tall fathers was lower than the
                                   mean height of their fathers and the mean height of sons of short fathers was higher than the
                                   mean height of their fathers. In this way, a tendency of the human race to regress or to return to
                                   a normal height was observed. Sir  Francis Galton referred this  tendency of returning to the
                                   mean height of all men as regression in his research paper, “Regression towards mediocrity in
                                   hereditary stature”. The term ‘Regression’, originated in this particular context, is now used in
                                   various fields of study, even though there may be no existence of any regressive tendency.
                                   9.4.1 Simple  Regression


                                   For a bivariate data (X , Y ), i = 1, 2, ...... n, we can have either X or Y as independent variable. If
                                                     i  i
                                   X is independent variable then we can estimate the average values of Y for a given value of X.
                                   The relation used for such estimation is called regression of Y on X. If on the other hand Y is used
                                   for estimating the average values of X, the relation will be called regression of  X on Y. For a




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