4 Calculating other probabilities

In this Section we see how to calculate probabilities represented by areas other than those of the type shown in Figure 3.

4.1 Case 1

Figure 4 illustrates what we do if both Z values are positive. By using the properties of the standard normal distribution we can organise matters so that any required area is always of ‘standard form’.

Figure 4

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Example 6

Find the probability that Z takes values between 1 and 2.

Solution

Using Table 1:

P ( Z = z 2 ) i.e. P ( Z = 2 ) is 0.4772

P ( Z = z 1 ) i.e. P ( Z = 1 ) is 0.3413.

Hence P ( 1 < Z < 2 ) = 0.4772 0.3413 = 0.1359

Remember that with a continuous distribution, P ( Z = 1 ) is meaningless (will have zero probability) so that P ( 1 Z 2 ) is interpreted as P ( 1 < Z < 2 ) .

4.2 Case 2

The following diagram illustrates the procedure to be followed when finding probabilities of the form P ( Z > z 1 ) .

Figure 5

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Example 7

What is the probability that Z > 2 ?

Solution

P ( 0 < Z < 2 ) = 0.4772 (from Table 1). Hence the probability is 0.5 0.4772 = 0.0228 .

4.3 Case 3

Here we consider the procedure to be followed when calculating probabilities of the form P ( Z < z 1 ) . Here the shaded area is the sum of the left-hand half of the total area and a ‘standard’ area.

Figure 6

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Example 8

What is the probability that Z < 2 ?

Solution

P ( Z < 2 ) = 0.5 + 0.4772 = 0 . 9772 .

4.4 Case 4

Here we consider what needs to be done when calculating probabilities of the form

P ( z 1 < Z < 0 ) where z 1 is positive. This time we make use of the symmetry in the standard normal distribution curve.

Figure 7

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Example 9

What is the probability that 2 < Z < 0 ?

Solution

The area is equal to that corresponding to P ( 0 < Z < 2 ) = 0 . 4772 .

4.5 Case 5

Finally we consider probabilities of the form P ( z 2 < Z < z 1 ) . Here we use the sum property and the symmetry property.

Figure 8

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Example 10

What is the probability that 1 < Z < 2 ?

Solution

P ( 1 < Z < 0 ) = P ( 0 < Z < 1 ) = 0.3413 P ( 0 < Z < 2 ) = 0.4772

Hence the required probability P ( 1 < Z < 2 ) is 0.8185.

Other cases can be handed by a combination of the ideas already used.

Task!

Find the following probabilities.

  1. P ( 0 < Z < 1.5 )
  2. P ( Z > 1.8 )
  3. P ( 1.5 < Z < 1.8 )
  4. P ( Z < 1.8 )
  5. P ( 1.5 < Z < 0 )
  6. P ( Z < 1.5 )
  7. P ( 1.8 < Z < 1.5 )
  8. P ( 1.5 < Z < 1.8 )

(A simple sketch of the standard normal curve will help.)

  1. 0.4332 (direct from Table 1)
  2. 0.5 0.4641 = 0.0359
  3. P ( 0 < Z < 1.8 ) P ( 0 < Z < 1.5 ) = 0.4641 0.4332 = 0.0309
  4. 0.5 + 0.4641 = 0.9641
  5. P ( 1.5 < Z < 0 ) = P ( 0 < Z < 1.5 ) = 0.4332
  6. P ( Z < 1.5 ) = P ( Z > 1.5 ) = 0.5 0.4332 = 0.0668
  7. P ( 1.8 < Z < 1.5 ) = P ( 1.5 < Z < 1.8 ) = 0.0309
  8. P ( 0 < Z < 1.5 ) + P ( 0 < Z < 1.8 ) = 0.8973