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Expectations and Moments                                         97

           "  tends to zero. In other words, random variable Y/n approaches the true mean
           with probability 1.
             Answer: to proceed with the proof of Equation (4.44), we first note that, if   2
           is the variance of each X j , it follows from Equation (4.41) that

                                         2
                                               2
                                          ˆ n  :
                                         Y
           According to the Chebyshev inequality, given by Expression (4.17), for every
           k >  0, we have
                                                   n  2
                                  P…jY   nmj  k†      :
                                                   k 2
                                                     2
                                                  2
           For k ˆ "n,  the left-hand side is less than   / " n),  which tends to zero as
           n !1  . This establishes the proof.
             Note that this proof requires the existence of   2 . This is not necessary but
           more work is required without this restriction.
             Among many of its uses, statistical sampling is an example in which the law of
           large numbers plays an important role. Suppose that in a group of m families
           there  are  m j number  of  families  with  exactly  j  children  ( j ˆ  0, 1, . . . ,  and
           m 0 ‡  m 1 ‡  .. . ˆ  m). For a family chosen at random, the number of children is
           a random variable that assumes the value r with probability p r ˆ  m r /m. A sample
           of  n  families  among  this  group  represents  n  observed  independent  random
           variables X 1 , .. ., X n , with the same distribution. The quantity (X 1 ‡     ‡  X n )/n
           is the sample average, and the law of large numbers then states that, for
           sufficiently largesamples, the sample averageislikely to beclose to

                                     X        X
                                 m ˆ     rp r ˆ  rm r =m;
                                      rˆ0     rˆ0
           the mean of the population.

             Example 4.13. The random variable Y /n in Example 4.12 is also called the



           sample mean associated with random variables X 1 ,..., X n  and is denoted by X .
           In Example 4.12, if the coefficient of variation for each X i  is v, the coefficient of
           variation v of X  is easily derived from Equations (4.38) and (4.41) to be
                    X
                                              v
                                        v ˆ                              …4:45†
                                         X    1=2
                                             n
                                               p
             Equation (4.45) is the basis for the law of    by Schr ödinger, which states that
                                                 n
           the laws of physics are accurate within a probable relative error of the order of
           n  1/2 ,  where n is the number of molecules that cooperate in a physical process.
           Basically, what Equation (4.45) suggests is that, if the action of each molecule






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