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Listed below are student evaluation ratings of courses, where a rating of 5 is for "excellent." Using the bootstrap method with 200 bootstrap samples, construct a 90% confidence interval estimate of u. How does the result compare to the confidence interval 3.66

Listed below are student evaluation ratings of courses, where a rating of 5 is for "excellent." Using the bootstrap method with 200 bootstrap samples, construct a 90% confidence interval estimate of u. How does the result compare to the confidence interval 3.66

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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**Student Evaluation Ratings Analysis** Listed below are student evaluation ratings of courses, where a rating of 5 is for "excellent." Using the bootstrap method with 200 bootstrap samples, we aim to construct a 90% confidence interval estimate of μ. How does this result compare to the confidence interval 3.66 ≤ μ ≤ 4.18 constructed using only the original sample data? **Original Ratings** 3.8, 2.9, 4.0, 4.8, 3.0, 4.2, 3.6, 4.9, 4.3, 4.1, 4.3, 3.7, 3.2, 4.1, 3.9 **Procedure Details** 1. **Bootstrap Interval Calculation:** - The bootstrap interval is represented as □ < μ < □. - (Type integers or decimals rounded to two decimal places as needed.) 2. **Bootstrap Samples Overview:** - The diagram represents a table of bootstrap samples, each consisting of different sequences of the original ratings. These samples were generated to understand the variability and distribution of the data better. **Bootstrap Samples Table** - Each row in the table corresponds to a different bootstrap sample, with samples numbered from 1 to 25 (as shown in this partial table). - The data in each sample consist of ratings like 4.1, 4.9, 4.1, 4.8, 4.2, 3.7, 4.3, etc., appearing repeatedly in various orders. - The variability in these samples helps construct a more robust confidence interval. **Conclusion** Using the bootstrap method, we refine our understanding of the confidence interval for the student course evaluation ratings compared to traditional methods, providing valuable insights into data trends and variability.
Transcribed Image Text:**Student Evaluation Ratings Analysis** Listed below are student evaluation ratings of courses, where a rating of 5 is for "excellent." Using the bootstrap method with 200 bootstrap samples, we aim to construct a 90% confidence interval estimate of μ. How does this result compare to the confidence interval 3.66 ≤ μ ≤ 4.18 constructed using only the original sample data? **Original Ratings** 3.8, 2.9, 4.0, 4.8, 3.0, 4.2, 3.6, 4.9, 4.3, 4.1, 4.3, 3.7, 3.2, 4.1, 3.9 **Procedure Details** 1. **Bootstrap Interval Calculation:** - The bootstrap interval is represented as □ < μ < □. - (Type integers or decimals rounded to two decimal places as needed.) 2. **Bootstrap Samples Overview:** - The diagram represents a table of bootstrap samples, each consisting of different sequences of the original ratings. These samples were generated to understand the variability and distribution of the data better. **Bootstrap Samples Table** - Each row in the table corresponds to a different bootstrap sample, with samples numbered from 1 to 25 (as shown in this partial table). - The data in each sample consist of ratings like 4.1, 4.9, 4.1, 4.8, 4.2, 3.7, 4.3, etc., appearing repeatedly in various orders. - The variability in these samples helps construct a more robust confidence interval. **Conclusion** Using the bootstrap method, we refine our understanding of the confidence interval for the student course evaluation ratings compared to traditional methods, providing valuable insights into data trends and variability.
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