For x‾1=40,σ1=8.3,n1=40,x‾2=39,σ2=5.8\overline{x}_1=40,\sigma_1=8.3,n_1=40,\overline{x}_2=39,\sigma_2=5.8, & n2=50n_2=50, perform a hypothesis test to test the claim that μ1>μ2\mu_1>\mu_2 for α=0.05\alpha=0.05.

P − v a l u e > α P-value>\alpha P − v a l u e > α ; fail to reject H 0 H_0 H 0 since there is not enough evidence to suggest μ 1 > μ 2 \mu_1>\mu_2 μ 1 > μ 2 .

For x ‾ 1 = 40 , σ 1 = 8.3 , n 1 = 40 , x ‾ 2 = 39 , σ 2 = 5.8 \overline{x}_1=40,\sigma_1=8.3,n_1=40,\overline{x}_2=39,\sigma_2=5.8 , & n 2 = 50 n_2=50 , perform a hypothesis test to test the claim that μ 1 > μ 2 \mu_1>\mu_2 for α = 0.05 \alpha=0.05 .

Welcome back. For this problem, we've been given two population standard deviations, and for a sample from each of those populations, we have a mean and a sample size. We'd like to perform a hypothesis test to test the claim that mu one is greater than mu two for alpha equals point zero five. So let's start by writing our hypothesis. The null hypothesis is that both means are the same, mu one is equal to mu two. And the alternative hypothesis is what we're trying to test, which in this case is that mu one is greater than mu two. Now that we have our hypothesis, let's turn to our t I 80 fours and run the test. We'll click on the stat button, scroll two to the right to the test menu, and click on three. That's step one. All set. We wanna enter in statistics and parameters, so we'll scroll over to stats and click enter. Then we'll put in the information we have from the problem. Sigma one is 8.3, and sigma two is 5.8. Then we'll enter in the information for our first sample, which has a mean of 40 and a size of 40, and the information for the second sample, which has a mean of 39 and a size of 50. Once that's all set, we'll select the correct alternative hypothesis, which is that mu one is greater than mu two. So we want greater than mu two selected. Then we'll scroll down to calculate and click enter. That's step two, all set. And we'll end up getting the p value in our viewing window, which is approximately 0.26. We wanna compare that to 0.05, which is our alpha value. We can see our p value is greater than alpha, so we fail to reject our null hypothesis, which means that there is not enough evidence to suggest that the alternative hypothesis is true, or that mu one is greater than mu two. Great work.

For x ‾ 1 = 40 , σ 1 = 8.3 , n 1 = 40 , x ‾ 2 = 39 , σ 2 = 5.8 \overline{x}_1=40,\sigma_1=8.3,n_1=40,\overline{x}_2=39,\sigma_2=5.8 , & n 2 = 50 n_2=50 , perform a hypothesis test to test the claim that μ 1 > μ 2 \mu_1>\mu_2 for α = 0.05 \alpha=0.05 .

P − v a l u e > α P-value>\alpha P − v a l u e > α ; fail to reject H 0 H_0 H 0 since there is not enough evidence to suggest μ 1 > μ 2 \mu_1>\mu_2 μ 1 > μ 2 .

P − v a l u e < α P-value<\alpha P − v a l u e < α ; fail to reject H 0 H_0 H 0 since there is not enough evidence to suggest μ 1 > μ 2 \mu_1>\mu_2 μ 1 > μ 2 .

P − v a l u e > α P-value>\alpha P − v a l u e > α ; reject H 0 H_0 H 0 since there is enough evidence to suggest μ 1 > μ 2 \mu_1>\mu_2 μ 1 > μ 2 .

P − v a l u e < α P-value<\alpha P − v a l u e < α ; reject H 0 H_0 H 0 since there is enough evidence to suggest μ 1 > μ 2 \mu_1>\mu_2 μ 1 > μ 2 .

10. Hypothesis Testing for Two Samples

Two Means - Known Variance

Statistics for Business

10. Hypothesis Testing for Two Samples

Two Means - Known Variance

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Multiple Choice

For ${\overline{x}}_{1} = 40, σ_{1} = 8.3, n_{1} = 40, {\overline{x}}_{2} = 39, σ_{2} = 5.8$ , & $n sub 2 equals 50$ , perform a hypothesis test to test the claim that $mu sub 1 is greater than mu sub 2$ for $alpha equals 0.05$ .

$P-value<\alpha$ ; fail to reject $H_0$ since there is not enough evidence to suggest $\mu_1>\mu_2$ .

$P-value>\alpha$ ; reject $H_0$ since there is enough evidence to suggest $\mu_1>\mu_2$ .

$P-value<\alpha$ ; reject $H_0$ since there is enough evidence to suggest $\mu_1>\mu_2$ .

$P-value>\alpha$ ; fail to reject $H_0$ since there is not enough evidence to suggest $\mu_1>\mu_2$ .

Verified step by step guidance

Step 1: Define the null and alternative hypotheses. Here, the claim is that the mean of population 1 is greater than the mean of population 2, so set $ H_0: \mu_1 \leq \mu_2 $ and $ H_a: \mu_1 > \mu_2 $.

Step 2: Identify the given sample statistics: $ \overline{x}_1 = 40 $, $ \sigma_1 = 8.3 $, $ n_1 = 40 $, $ \overline{x}_2 = 39 $, $ \sigma_2 = 5.8 $, and $ n_2 = 50 $.

Step 3: Calculate the test statistic for the difference between two means using the formula: \[ z = \frac{(\overline{x}_1 - \overline{x}_2) - (\mu_1 - \mu_2)}{\sqrt{\frac{\sigma_1^2}{n_1} + \frac{\sigma_2^2}{n_2}}} \] where under the null hypothesis, $ \mu_1 - \mu_2 = 0 $.

Step 4: Determine the p-value corresponding to the calculated z-test statistic for a right-tailed test (since $ H_a: \mu_1 > \mu_2 $).

Step 5: Compare the p-value to the significance level $ \alpha = 0.05 $. If $ \text{p-value} < \alpha $, reject $ H_0 $; otherwise, fail to reject $ H_0 $.

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