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You wish to test the following claim [tex]\((H_a)\)[/tex] at a significance level of [tex]\(\alpha=0.05\)[/tex].

[tex]\[

\begin{array}{l}

H_o: \mu_1=\mu_2 \\

H_a: \mu_1\ \textless \ \mu_2

\end{array}

\][/tex]

You obtain the following two samples of data.

Sample #1

[tex]\[

\begin{array}{|r|r|r|r|}

\hline 80.8 & 86.7 & 81.4 & 91.6 \\

\hline 89.3 & 87.1 & 105.6 & 86 \\

\hline 101.5 & 94.8 & 85.4 & 93.9 \\

\hline 88.4 & 89.3 & 81.1 & 93.7 \\

\hline 88.2 & 85 & 82 & 92 \\

\hline 95.3 & 100.8 & 86.6 & 80.4 \\

\hline 79.6 & 87.5 & 85.4 & 87.8 \\

\hline 72.6 & 91.5 & 85.8 & 83.9 \\

\hline 93.5 & 90.4 & 100.8 & 84.7 \\

\hline 81.7 & 82.3 & 86.6 & 99.1 \\

\hline 74.5 & 94.6 & 89.1 & 90.7 \\

\hline 92.8 & 95.6 & 83.9 & 86.4 \\

\hline 95.3 & 83.1 & 86.4 & 91.5 \\

\hline

\end{array}

\][/tex]

Sample #2

[tex]\[

\begin{array}{|r|r|r|r|}

\hline 100.4 & 111.8 & 72.4 & 81.1 \\

\hline 84.5 & 74 & 96.5 & 110.2 \\

\hline 77.4 & 91.8 & 120.2 & 67.1 \\

\hline 102.1 & 102.1 & 96 & 90.3 \\

\hline 92.9 & 81.1 & 57.4 & 87.7 \\

\hline 93.9 & 67.1 & 111.8 & 98.1 \\

\hline 81.7 & 100.4 & 72.4 & 113.6 \\

\hline 78.7 & 106.5 & 107.2 & 88.7 \\

\hline 96.5 & 89.2 & 109.4 & 115.5 \\

\hline 105.8 & 89.2 & 127.4 & 105.2 \\

\hline 120.2 & 100.9 & 99.8 & 116.6 \\

\hline 74.7 & 90.3 & & \\

\hline

\end{array}

\][/tex]

What is the test statistic for this sample? (Report the answer accurate to three decimal places.)

test statistic = [tex]\(\square\)[/tex]

What is the [tex]\(p\)[/tex]-value for this sample? For this calculation, use the degrees of freedom reported from the technology you are using. (Report the answer accurate to four decimal places.)

p-value = [tex]\(\square\)[/tex]

Answer :

To test the claim [tex]\(H_a: \mu_1 < \mu_2\)[/tex] at a significance level of [tex]\(\alpha = 0.05\)[/tex], using two samples of data, follow these steps:

1. State the Hypotheses:
- Null Hypothesis ([tex]\(H_0\)[/tex]): [tex]\(\mu_1 = \mu_2\)[/tex]
- Alternative Hypothesis ([tex]\(H_a\)[/tex]): [tex]\(\mu_1 < \mu_2\)[/tex]

2. Collect Sample Data:
- Sample 1: Composed of 52 data points.
- Sample 2: Composed of 46 data points.

3. Calculate Sample Means and Standard Deviations:
- For Sample 1, compute the mean and standard deviation.
- For Sample 2, compute the mean and standard deviation.

4. Compute the Standard Error of Difference (SED):
[tex]\[
\text{SED} = \sqrt{\left(\frac{\text{std1}^2}{n1}\right) + \left(\frac{\text{std2}^2}{n2}\right)}
\][/tex]
where [tex]\(\text{std1}\)[/tex] and [tex]\(\text{std2}\)[/tex] are the standard deviations of Sample 1 and Sample 2, respectively, and [tex]\(n1\)[/tex] and [tex]\(n2\)[/tex] are their sizes.

5. Calculate the Test Statistic:
[tex]\[
t = \frac{\text{mean1} - \text{mean2}}{\text{SED}}
\][/tex]

6. Determine Degrees of Freedom (df):
- The degrees of freedom are determined to be equal to the smaller of [tex]\(n1 - 1\)[/tex] and [tex]\(n2 - 1\)[/tex].

7. Find the p-value:
- Using the calculated [tex]\(t\)[/tex]-statistic and degrees of freedom, find the [tex]\(p\)[/tex]-value from the [tex]\(t\)[/tex]-distribution for a one-tailed test.

Here is the result for this specific test:

- Test Statistic: [tex]\(-2.437\)[/tex]
- p-value: [tex]\(0.0094\)[/tex]

Conclusion:
Since the p-value ([tex]\(0.0094\)[/tex]) is less than the significance level ([tex]\(\alpha = 0.05\)[/tex]), we reject the null hypothesis [tex]\(H_0\)[/tex]. There is significant evidence to support the claim that [tex]\(\mu_1 < \mu_2\)[/tex].

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