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Kirstyn conducted an experiment to determine if high school athletes run faster after warming up. She randomly selected 30- athletes from her large school and had them run a 100-meter sprint with and without warming up (one week apart), randoml determining which they ran first. For each runner, she calculated the difference (No warm-up- Warm-up) in 100-meter sprim times, in seconds. The mean of the differences is an = 1.293 seconds and the standard deviation is sar 0.438 seconds. (a) Why should you use paired t procedures to perform inference about a mean difference in this setting? The subjects were randomly assigned to treatment groups. O The experiment used a control group. There was a random sample of athletes. The data come from a matched pairs experiment with the order of the two treatments being randomly assigned to each athlete. (b) Is there convincing evidence at the a=0.05 significance level that athletes from this school run faster (have shorter times), on average, when they warm up? STATE: Ho H where athletes like these. Use a =0.05. 0 0 the true mean difference (No warm-up- Warm-up) in 100-meter sprint times (seconds) for The evidence for Ha is: -1.293 0.

Kirstyn conducted an experiment to determine if high school athletes run faster after warming up. She randomly selected 30- athletes from her large school and had them run a 100-meter sprint with and without warming up (one week apart), randoml determining which they ran first. For each runner, she calculated the difference (No warm-up- Warm-up) in 100-meter sprim times, in seconds. The mean of the differences is an = 1.293 seconds and the standard deviation is sar 0.438 seconds. (a) Why should you use paired t procedures to perform inference about a mean difference in this setting? The subjects were randomly assigned to treatment groups. O The experiment used a control group. There was a random sample of athletes. The data come from a matched pairs experiment with the order of the two treatments being randomly assigned to each athlete. (b) Is there convincing evidence at the a=0.05 significance level that athletes from this school run faster (have shorter times), on average, when they warm up? STATE: Ho H where athletes like these. Use a =0.05. 0 0 the true mean difference (No warm-up- Warm-up) in 100-meter sprint times (seconds) for The evidence for Ha is: -1.293 0.

MATLAB: An Introduction with Applications
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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DO: The conditions for inference are met. There was no random sampling. Two sample I test for μ₁ - H2 The conditions for inference are not met. Paired t test for Ha The Normal/Large Sample condition is met because a = 30 ≥ 30. There were two separate random samples of 30 athletes. Paired t test for Xa Round your answer to 2 decimal places. 1= P-value= CONCLUDE: Because the P-value is a = 0.05, we that athletes from this school can run faster, on average, after warming up. Ho. There convincing evidence (c) Can we conclude that runners at this school will run faster by warming up? Justify your answer. and effect. , which us to draw conclusions about cause (d) Based on your conclusion in part (b), which type of error-a Type I error or a Type II error-could you have made? Because we failed to reject the null hypothesis it is possible that we could have made a Type II error. Because we failed to reject the null hypothesis it is possible that we could have made a Type I error. Because we rejected the null hypothesis it is possible that we could have made a Type II error. Because we rejected the null hypothesis it is possible that we could have made a Type I error. MacBook Air
Transcribed Image Text:DO: The conditions for inference are met. There was no random sampling. Two sample I test for μ₁ - H2 The conditions for inference are not met. Paired t test for Ha The Normal/Large Sample condition is met because a = 30 ≥ 30. There were two separate random samples of 30 athletes. Paired t test for Xa Round your answer to 2 decimal places. 1= P-value= CONCLUDE: Because the P-value is a = 0.05, we that athletes from this school can run faster, on average, after warming up. Ho. There convincing evidence (c) Can we conclude that runners at this school will run faster by warming up? Justify your answer. and effect. , which us to draw conclusions about cause (d) Based on your conclusion in part (b), which type of error-a Type I error or a Type II error-could you have made? Because we failed to reject the null hypothesis it is possible that we could have made a Type II error. Because we failed to reject the null hypothesis it is possible that we could have made a Type I error. Because we rejected the null hypothesis it is possible that we could have made a Type II error. Because we rejected the null hypothesis it is possible that we could have made a Type I error. MacBook Air
BFW Publishers Kirstyn conducted an experiment to determine if high school athletes run faster after warming up. She randomly selected 30 athletes from her large school and had them run a 100-meter sprint with and without warming up (one week apart), randomly determining which they ran first. For each runner, she calculated the difference (No warm-up- Warm-up) in 100-meter sprint times, in seconds. The mean of the differences is a = 1.293 seconds and the standard deviation is s&r=0.438 seconds. (a) Why should you use paired t procedures to perform inference about a mean difference in this setting? The subjects were randomly assigned to treatment groups. The experiment used a control group. There was a random sample of athletes. The data come from a matched pairs experiment with the order of the two treatments being randomly assigned to each athlete. (b) Is there convincing evidence at the a=0.05 significance level that athletes from this school run faster (have shorter times), on average, when they warm up? STATE: Ho H. where athletes like these. 0 0 =the true mean difference (No warm-up- Warm-up) in 100-meter sprint times (seconds) for Use a 0.05. The evidence for H. is: = 1.293 0. PLAN: Select the 4 true statements. Two sample r test for x-x The sample size is small, but the dotplot of the differences doesn't show any outliers or strong skewness. The conditions for inference are met. There was no random sampling. ↓ Two sample t test for MacBook Air Random sample of 30 athletes from this school. Kirstyn also randomly assigned a treatment order to each athlete. The Normal/ Large Sample condition is met because 302 10. Paired t test for Had The Normal/Large Sample condition is met because Mai 30 ≥ 30. There were two separate random samples of 30 athletes,
Transcribed Image Text:BFW Publishers Kirstyn conducted an experiment to determine if high school athletes run faster after warming up. She randomly selected 30 athletes from her large school and had them run a 100-meter sprint with and without warming up (one week apart), randomly determining which they ran first. For each runner, she calculated the difference (No warm-up- Warm-up) in 100-meter sprint times, in seconds. The mean of the differences is a = 1.293 seconds and the standard deviation is s&r=0.438 seconds. (a) Why should you use paired t procedures to perform inference about a mean difference in this setting? The subjects were randomly assigned to treatment groups. The experiment used a control group. There was a random sample of athletes. The data come from a matched pairs experiment with the order of the two treatments being randomly assigned to each athlete. (b) Is there convincing evidence at the a=0.05 significance level that athletes from this school run faster (have shorter times), on average, when they warm up? STATE: Ho H. where athletes like these. 0 0 =the true mean difference (No warm-up- Warm-up) in 100-meter sprint times (seconds) for Use a 0.05. The evidence for H. is: = 1.293 0. PLAN: Select the 4 true statements. Two sample r test for x-x The sample size is small, but the dotplot of the differences doesn't show any outliers or strong skewness. The conditions for inference are met. There was no random sampling. ↓ Two sample t test for MacBook Air Random sample of 30 athletes from this school. Kirstyn also randomly assigned a treatment order to each athlete. The Normal/ Large Sample condition is met because 302 10. Paired t test for Had The Normal/Large Sample condition is met because Mai 30 ≥ 30. There were two separate random samples of 30 athletes,
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