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Section 4-6/Normal Distribution     121


                                          f (x)








                                               m – 3s m – 2s  m – s  m  m + s m + 2s m + 3s  x
                                                                68%
                                                                95%
                                                               99.7%
                                         FIGURE 4-12  Probabilities associated with a normal distribution.


                     Example 4-11    Standard Normal Distribution  Assume that Z is a standard normal random variable. Appendix
                                                                                                            (
                                                                          z
                                     Table III provides probabilities of the form Φ( ) = (Z  ≤ ). The use of Table III to i nd P Z ≤ . ) 5
                                                                              P
                                                                                   z
                                                                                                                1
                     is illustrated in Fig. 4-13. Read down the z column to the row that equals 1.5. The probability is read from the adjacent
                     column, labeled 0.00, to be 0.93319.
                                                      P(Z # 1.5) = F(1.5)  z  0.00  0.01  0.02  0.03
                                                      = shaded area
                                                                      0  0.50000  0.50399  0.50398 0.51197
                                                                      . . .        . . .
                                                                    1.5  0.93319  0.93448  0.93574 0.93699
                                                 0     1.5   z
                                     FIGURE 4-13  Standard normal probability density function.
                                                                                                  (
                                                                               (
                        The column headings refer to the hundredths digit of the value of z in P Z ≤  z). For example, P Z ≤ . ) is found by
                                                                                                      1 53
                     reading down the z column to the row 1.5 and then selecting the probability from the column labeled 0.03 to be 0.93699.

                                                                           (
                                            Probabilities that are not of the form P Z #  z) are found by using the basic rules of prob-
                                         ability and the symmetry of the normal distribution along with Appendix Table III. The
                                           following examples illustrate the method.


                     Example 4-12    The following calculations are shown pictorially in Fig. 4-14. In practice, a probability is often
                                     rounded to one or two signii cant digits.
                     (1)  P Z > 1 26) = − (  1 26) = 1 0 89616 = =  0 10384.
                          (
                                              .
                               .
                                                       .
                                                                 .
                                                    −
                                       P Z ≤
                                    1
                          (
                     (2)  P Z < 20 86) = 0 19490.
                                .
                                       .
                          (
                     (3)  P Z > − . ) = (  1  37  0
                                      P Z < . ) = .91465.
                                1 37
                                      . ). This probability can be found from the difference of two areas, P Z < 0 37) − (
                          (
                     (4)  P − .25 <  Z <  0 37                                               (    .     P Z < −  1 25).
                                                                                                                .
                            1
                     Now,
                                                             (
                                                           P Z < 0 37. ) = .
                                                                       0 64431
                     and
                                                            (
                                                           P Z < − . ) = .1 25  0 10565
                     Therefore,
                                                 (
                                               P − . 1 25 <  Z <  0 . ) = .37  0 64431 − .10565  = .53866
                                                                                   0
                                                                          0
                          (
                     (5)  P Z ≤ − . ) 6  cannot be found exactly from Appendix Table III. However, the last entry in the table can be used to
                                4
                              (
                                                                                    (
                                                                       (
                                                           (
                                     99
                                    3
                     i nd that P Z ≤ − . ) = 0 .00003 Because P Z ≤ − . ) 6 < P Z ≤ − . ) , P Z ≤ − . ) 6  is nearly zero.
                                                                4
                                                                             3 99
                                                                                          4
                                                 .
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