Simulation Study on Confidence Interval Estimation for Standard Deviation with Non-Normal Distributions

<p>This study explores innovative approaches to constructing confidence intervals for the population standard deviation, σ, in non-normal data scenarios. While the sample standard deviation, s, is widely used, its reliability is compromised when dealing with skewed or heavy-tailed distributions and exhibits sensitivity to outliers. Our research addresses these limitations by investigating alternative estimation methods that offer greater robustness and accuracy.</p>

Name thesis:degree_name

Master of Science - Mathematical Sciences

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

College of Science and Mathematics

Year dc:date.available

2024

Author dc:creator

• Oppong Kyeremeh, Theophilus

Contributors dc:contributor

• Robert Henderson
• Jane Long
• Jacob Turner

• Confidence Interval
• Standard deviation
• Sample size
• Simulation
• Robust
• Bootstrap
• Coverage probability
• Applied Statistics
• Probability
• Statistical Methodology
• Statistical Theory

Repository record dc:identifier

https://scholarworks.sfasu.edu/etds/552

OAI identifier oai:identifier

oai:scholarworks.sfasu.edu:etds-1620