slide 2 - 60 copyright © 2008 pearson education, inc. chapter 9 hypothesis tests for one population...

Copyright © 2008 Pearson Education, Inc. Slide 2 - 60

Chapter 9

Hypothesis Tests for

One Population Mean


Definition 9.1


Example 9.4

A company that produces snack foods uses a machine to package 454 g bags of pretzels. We assume that the net weights are normally distributed and that the population standard deviation of all such weights is 7.8 g. A simple random sample of 25 bags of pretzels has the net weights, in grams, displayed in Table 9.1. Do the data provide sufficient evidence to conclude that the packaging machine is not working properly? We use the following steps to answer the question.

Table 9.1


Example 9.4

a. State the null and alternative hypotheses for the hypothesis test.

b. Discuss the logic of this hypothesis test.c. Identify the distribution of the variable , that is, the

sampling distribution of the sample mean for samples of size 25.

d. Obtain a precise criterion for deciding whether to reject the null hypothesis in favor of the alternative hypothesis.

e. Apply the criterion in part (d) to the sample data and state the conclusion.

x 26 1.6451.4

3025.6


Solution Example 9.4

a. The null and alternative hypotheses, as stated in Example 9.1, areH0 : μ = 454 g (the packaging machine is working properly)Ha : μ = 454 g (the packaging machine is not working properly).

• If the null hypothesis is true, then the mean weight, , of the sample of 25 bags of pretzels should approximately equal 454 g. However, if the sample mean weight differs “too much” from 454 g, we would be inclined to reject the null hypothesis and conclude that the alternative hypothesis is true. As we show in part (d), we can use our knowledge of the sampling distribution of the sample mean to decide how much difference is “too much.”

x



c. n = 25, σ = 7.8, weights are normally distributed,･ μ = μ (which we don’t know),･ σ = and･ is normally distributed.In other words, for samples of size 25, the variable is normally distributed with mean μ and standard deviation 1.56 g.

d. The “95.44” part of the 68.26–95.44–99.74 rule states that, for a normally distributed variable, 95.44% of all possible observations lie within two standard deviations to either side of the mean. Applying this part of the rule to the variable and referring to part (c), we see that 95.44% of all samples of 25 bags of pretzels have mean weights within 2•1.56 = 3.12 g of μ.

xx n 7.8 25 1.56x



d. Equivalently, only 4.56% of all samples of 25 bags of pretzels have mean weights that are not within 3.12 g of μ, as illustrated in Fig. 9.1.

Figure 9.1



d. If the mean weight, , of the 25 bags of pretzels sampled is more than two standard deviations (3.12 g) from 454 g, reject the null hypothesis and conclude that the alternative hypothesis is true. Otherwise, do not reject the null hypothesis.

Figure 9.2

x



e. The mean weight, , of the sample of 25 bags of pretzels whose weights are given in Table 9.1 is 450g. So, z =( − 454) / 1.56 = (450 − 454) /1.56 = −2.56.That is, the sample mean of 450 g is 2.56 standard deviations below the null-hypothesis population mean of 454 g, as shown in Fig. 9.3.

Figure 9.3

x

x



e. Because the mean weight of the 25 bags of pretzels sampled is more than two standard deviations from 454 g, we reject the null hypothesis (μ = 454 g) and conclude that the alternative hypothesis (μ = 454 g) is true.

The data provide sufficient evidence to conclude that the packaging machine is not working properly.


Definition 9.2


For a two-tailed test, the null hypothesis is rejected when the test statistic is either too small or too large. The rejection region consists of two parts: one on the left and one on the right, (Fig. 9.6(a)).

For a left-tailed test, the null hypothesis is rejected only when the test statistic is too small. The rejection region consists of only one part, on the left, (Fig.9.6(b)).

For a right-tailed test, the null hypothesis is rejected only when the test statistic is too large. The rejection region consists of only one part, on the right, (Fig.9.6(c)).

Figure 9.6


Definition 9.3


Definition 9.4


Key Fact 9.1


Key Fact 9.2


Key Fact 9.3


Procedure 9.1


Procedure 9.1 (cont.)


Key Fact 9.4


Definition 9.5

Copyright © 2008 Pearson Education, Inc. Slide 23 - 60Figure 9.14

If the null hypothesis is true, this test statistic has the standard normal distribution, and its probabilities equal areas under the standard normal curve. If we let z0 denote the observed value of the test statistic z, we obtain the P-value as follows: Two-tailed test: The P-value is the probability of observing a value of the test statistic z that is at least as large in magnitude as the value actually observed, which is the area under the standard normal curve that lies outside the interval from − |z0 | to |z0 |, as in Fig. 9.14(a).


Figure 9.14

Left-tailed test: The P-value is the probability of observing a value of the test statistic z that is as small as or smaller than the value actually observed, which is the area under the standard normal curve that lies to the left of z0, as in Fig. 9.14(b).Right-tailed test: The P-value is the probability of observing a value of the test statistic z that is as large as or larger than the value actually observed, which is the area under the standard normal curve that lies to the right of z0, as in Fig. 9.14(c).

Copyright © 2008 Pearson Education, Inc. Slide 25 - 60Figure 9.17

hypothesis would be rejected for a test at the 0.05 significance level but would not be rejected for a test at the 0.01 significance level. In fact, the P-value is precisely the smallest significance level at which the null hypothesis would be rejected.

The P-value can be interpreted as the observed significance level of a hypothesis test. Suppose that the value of the test statistic for a right-tailed z-test turns out to be 1.88. Then the P-value of the hypothesis test is 0.03 (actually 0.0301), the shaded area in Fig. 9.17. The null


Procedure 9.2


Table 9.9


Example 9.14

Use Table IV to estimate the P-value of each one-mean t-test.• Left-tailed test, n = 12, and t = −1.938b. Two-tailed test, n = 25, and t = −0.895

Solution a. Because the test is left-tailed, the P-value is the area under the t-curve with df = 12−1 = 11 that lies to the left of −1.938, as shown in Fig. 9.20(a).

Figure 9.20



A t-curve is symmetric about 0, so the area to the left of −1.938 equals the area to the right of 1.938, which we can estimate by using Table IV. In the df = 11 row of Table IV, the two t-values that straddle 1.938 aret0.05 = 1.796 and t0.025 = 2.201. Therefore the area under the t-curve that lies to the right of 1.938 is between 0.025 and 0.05, as shown in Fig. 9.20(b). Consequently, the area under the t-curve that lies to the left of −1.938 is also between 0.025 and 0.05, so 0.025 < P < 0.05. Hence we can reject H0 at any significance level of 0.05 or larger, and we cannot reject H0 at any significance level of 0.025 or smaller. For significance levels between 0.025 and 0.05, Table IV is not sufficiently detailed to help us to decide whether to reject H0.



b. Because the test is two tailed, the P-value is the area under the t-curve with df = 25 − 1 = 24 that lies either to the left of −0.895 or to the right of 0.895, as shown in Fig. 9.21(a).

Figure 9.21



Because a t-curve is symmetric about 0, the areas to the left of −0.895 and to the right of 0.895 are equal. In thedf = 24 row of Table IV, 0.895 is smaller than any othert-value, the smallest being t0.10 = 1.318. The area under the t-curve that lies to the right of 0.895, therefore, is greater than 0.10, as shown in Fig. 9.21(b). Consequently, the area under the t-curve that lies either to the left of −0.895 or to the right of 0.895 is greater than 0.20, so P > 0.20. Hence we cannot reject H0 at any significance level of 0.20 or smaller. For significance levels larger than 0.20, Table IV is not sufficiently detailed to help us to decide whether to reject H0.


Procedure 9.3

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