Use this fact to derive an exact 95% confidence interval for the population variance o?.
Use this fact to derive an exact 95% confidence interval for the population variance o?.
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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![### Theorem: Variance and Chi-Square Distribution
Let \( X_1, X_2, \ldots, X_n \) be a random sample from a normal distribution with mean \( \mu \) and variance \( \sigma^2 \). The expression for the sample variance \( s^2 \) is given by:
\[
\frac{(n-1)s^2}{\sigma^2} = \frac{\sum_{i=1}^n (X_i - \overline{X})^2}{\sigma^2}
\]
This expression follows a chi-square (\(\chi^2\)) distribution with \( n-1 \) degrees of freedom. Here, \( \overline{X} \) is the sample mean, and \( s^2 = \frac{1}{n-1}\sum_{i=1}^n (X_i - \overline{X})^2 \) is the unbiased estimator of the population variance. Importantly, \( \overline{X} \) and \( s^2 \) are independent random variables.
### Application
Use this theorem to derive an exact 95% confidence interval for the population variance \( \sigma^2 \). Consider the properties of the chi-square distribution and the relation of sample variance to the population variance in your calculations.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F9ce5d786-26a3-49b3-8729-874f34afced3%2Ff9940c4d-974e-47be-b4a1-14e108519bf8%2Fa7cjau5_processed.jpeg&w=3840&q=75)
Transcribed Image Text:### Theorem: Variance and Chi-Square Distribution
Let \( X_1, X_2, \ldots, X_n \) be a random sample from a normal distribution with mean \( \mu \) and variance \( \sigma^2 \). The expression for the sample variance \( s^2 \) is given by:
\[
\frac{(n-1)s^2}{\sigma^2} = \frac{\sum_{i=1}^n (X_i - \overline{X})^2}{\sigma^2}
\]
This expression follows a chi-square (\(\chi^2\)) distribution with \( n-1 \) degrees of freedom. Here, \( \overline{X} \) is the sample mean, and \( s^2 = \frac{1}{n-1}\sum_{i=1}^n (X_i - \overline{X})^2 \) is the unbiased estimator of the population variance. Importantly, \( \overline{X} \) and \( s^2 \) are independent random variables.
### Application
Use this theorem to derive an exact 95% confidence interval for the population variance \( \sigma^2 \). Consider the properties of the chi-square distribution and the relation of sample variance to the population variance in your calculations.
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