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Statistics



                      Notes             Since the mean and the variance are finite, we can apply the weak law of large numbers for
                                         the sequence {X  : 1  i  n}. Then we have
                                                      1
                                                                   S     
                                                               P    n   p     0 as b  
                                                                          
                                                                   n     
                                                                                             S
                                         S  for every  > 0 where S  = X  + X  + ..... + X . Now, what is   n  ?  S  is the number of
                                                                                             n
                                          n                   n    1   2       n                  n
                                                                             S
                                         successes observed in n trials and therefore   n   is the proportinn of successes in n trials.
                                                                             n
                                         Then the above result says that as the number of trials increases, the proportion of successes
                                         tends stabilize to the probability of a success. Of course, one of  the basic assumptions
                                         behind this interpretation is that the random experiment can be repeated.

                                    11.3 Keywords

                                    Chebyshev’s inequality is discussed, as an application, weak law of large numbers is derived.
                                    Weak law of large nombers: Suppose X , X , ....., X  are i.i.d. random variables with mean m and
                                                                    1  2    n
                                                 2
                                    finite variance  .
                                    11.4 Self Assessment

                                    1.   Computation of the  probabilities, even  when the .................. functions are known,  is
                                         cumbersome at times.
                                         (a)  Chebyshev’s inequality   (b)  limiting  distributions
                                         (c)  (absolutely)  continuous  (d)  probability distribution
                                    2.   The .................. can be used for computation of the probabilities approximately.
                                         (a)  Chebyshev’s inequality   (b)  limiting  distributions

                                         (c)  (absolutely)  continuous  (d)  probability distribution
                                    3.   .................. is discussed, as an application, weak law of large numbers is derived.
                                         (a)  Chebyshev’s inequality   (b)  limiting  distributions

                                         (c)  (absolutely)  continuous  (d)  probability distribution
                                    4.   Chebyshev’s inequality also holds when the distribution of X is neither .................. nor
                                         discrete.

                                         (a)  Chebyshev’s inequality   (b)  limiting  distributions
                                         (c)  (absolutely)  continuous  (d)  probability distribution

                                    11..5 Review Questions

                                                                                  2
                                    1.   Suppose X is N(,  ). Then E(X) =  and Var(X) =  . Let us compute P[ |X – |  3].
                                                         2
                                    2.   Suppose X is a random variable such that P[X = 1] = 1/2 = P[X = –1]. Let us compute an
                                         upper bound for P[|X – |> 1/2].
                                    3.   Suppose a person makes 100 check transactions during a certain period. In balancing his or
                                         her check book transactions, suppose he or she rounds off the check entries to the nearest



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