Explain how confid explanation.
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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![**Title: Understanding Confidence Intervals in Decision-Making**
Provide an appropriate response.
**6) Application of Confidence Intervals in Decision-Making:**
Explain how confidence intervals might be used to make decisions. Give an example to clarify your explanation.
---
**Educational Explanation:**
Confidence intervals are a fundamental concept in statistics used to estimate a range in which we expect a population parameter to lie, based on sample data. They account for the degree of uncertainty or variability in the data.
**Example Explanation:**
Consider a 95% confidence interval for the proportion of female medical students in a university. If this interval is found to be between 0.45 and 0.55, decision-makers can interpret that there's a 95% certainty the true proportion of female medical students lies within this range.
This information can be pivotal for university policy decisions, such as gender-targeted scholarships or diversity initiatives. By understanding the potential range in the proportion, decision-makers can implement programs with greater confidence that these efforts align with the actual demographics.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fca98936e-33c7-4402-95ae-aeafe8e3f8b8%2Fdbd01511-81a3-4e80-bf45-54fb15eb815f%2F8vyb8n_processed.jpeg&w=3840&q=75)
Transcribed Image Text:**Title: Understanding Confidence Intervals in Decision-Making**
Provide an appropriate response.
**6) Application of Confidence Intervals in Decision-Making:**
Explain how confidence intervals might be used to make decisions. Give an example to clarify your explanation.
---
**Educational Explanation:**
Confidence intervals are a fundamental concept in statistics used to estimate a range in which we expect a population parameter to lie, based on sample data. They account for the degree of uncertainty or variability in the data.
**Example Explanation:**
Consider a 95% confidence interval for the proportion of female medical students in a university. If this interval is found to be between 0.45 and 0.55, decision-makers can interpret that there's a 95% certainty the true proportion of female medical students lies within this range.
This information can be pivotal for university policy decisions, such as gender-targeted scholarships or diversity initiatives. By understanding the potential range in the proportion, decision-makers can implement programs with greater confidence that these efforts align with the actual demographics.
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