**Problem Statement:** 6. A high school track and field coach wants to see if a new javelin model will help increase throw lengths of his students. The table below lists the average length in meters using the old model and the new model for his 5 student-athletes. | | Jerry | Tamar | Rodney | Alex | Jin | |-----------|-------|-------|--------|-------|-------| | Old Javelin | 43.7 | 41.1 | 47 | 41.8 | 41.9 | | New Javelin | 44.5 | 42.9 | 47.3 | 42.5 | 43.5 | | Difference \(d\) | -0.8 | -1.8 | -0.3 | -0.7 | -1.6 | | \((d - \overline{d})\) | 0.24 | -0.76 | 0.74 | 0.34 | -0.56 | | \((d - \overline{d})^2\) | 0.0576 | 0.5776 | 0.5476 | 0.1156 | 0.3136 | **Explanation:** - The "Old Javelin" and "New Javelin" rows show the average throw lengths in meters for each student. - The "Difference \(d\)" row shows the difference between the new and old javelin throws for each student. - The row \((d - \overline{d})\) shows the deviation of each difference from the mean difference. - The row \((d - \overline{d})^2\) represents the squared deviation of each difference from the mean difference. **Hypotheses:** - \(H_0: \mu_1 = \mu_2\) - \(H_a: \mu_1 < \mu_2\) (Claim) **Question:** At the 0.01 level of significance, is there enough evidence to reject \(H_0\) and support the manufacturer’s claim? **Options:** A. Yes, the test statistic \(t = -3.66\) is in the rejection region with critical value \(-3.746
**Problem Statement:** 6. A high school track and field coach wants to see if a new javelin model will help increase throw lengths of his students. The table below lists the average length in meters using the old model and the new model for his 5 student-athletes. | | Jerry | Tamar | Rodney | Alex | Jin | |-----------|-------|-------|--------|-------|-------| | Old Javelin | 43.7 | 41.1 | 47 | 41.8 | 41.9 | | New Javelin | 44.5 | 42.9 | 47.3 | 42.5 | 43.5 | | Difference \(d\) | -0.8 | -1.8 | -0.3 | -0.7 | -1.6 | | \((d - \overline{d})\) | 0.24 | -0.76 | 0.74 | 0.34 | -0.56 | | \((d - \overline{d})^2\) | 0.0576 | 0.5776 | 0.5476 | 0.1156 | 0.3136 | **Explanation:** - The "Old Javelin" and "New Javelin" rows show the average throw lengths in meters for each student. - The "Difference \(d\)" row shows the difference between the new and old javelin throws for each student. - The row \((d - \overline{d})\) shows the deviation of each difference from the mean difference. - The row \((d - \overline{d})^2\) represents the squared deviation of each difference from the mean difference. **Hypotheses:** - \(H_0: \mu_1 = \mu_2\) - \(H_a: \mu_1 < \mu_2\) (Claim) **Question:** At the 0.01 level of significance, is there enough evidence to reject \(H_0\) and support the manufacturer’s claim? **Options:** A. Yes, the test statistic \(t = -3.66\) is in the rejection region with critical value \(-3.746
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Question
Transcribed Image Text:**Problem Statement:**
6. A high school track and field coach wants to see if a new javelin model will help increase throw lengths of his students. The table below lists the average length in meters using the old model and the new model for his 5 student-athletes.
| | Jerry | Tamar | Rodney | Alex | Jin |
|-----------|-------|-------|--------|-------|-------|
| Old Javelin | 43.7 | 41.1 | 47 | 41.8 | 41.9 |
| New Javelin | 44.5 | 42.9 | 47.3 | 42.5 | 43.5 |
| Difference \(d\) | -0.8 | -1.8 | -0.3 | -0.7 | -1.6 |
| \((d - \overline{d})\) | 0.24 | -0.76 | 0.74 | 0.34 | -0.56 |
| \((d - \overline{d})^2\) | 0.0576 | 0.5776 | 0.5476 | 0.1156 | 0.3136 |
**Explanation:**
- The "Old Javelin" and "New Javelin" rows show the average throw lengths in meters for each student.
- The "Difference \(d\)" row shows the difference between the new and old javelin throws for each student.
- The row \((d - \overline{d})\) shows the deviation of each difference from the mean difference.
- The row \((d - \overline{d})^2\) represents the squared deviation of each difference from the mean difference.
**Hypotheses:**
- \(H_0: \mu_1 = \mu_2\)
- \(H_a: \mu_1 < \mu_2\) (Claim)
**Question:**
At the 0.01 level of significance, is there enough evidence to reject \(H_0\) and support the manufacturer’s claim?
**Options:**
A. Yes, the test statistic \(t = -3.66\) is in the rejection region with critical value \(-3.746
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