1. Confidence Intervals for a Standard Deviation
Section 8.4

2. Objectives Find critical values of the chi-square distribution
Construct confidence intervals for the variance and standard deviation of a normal distribution

3. Find critical values of the chi-square distribution
Objective 1
Find critical values of the chi-square distribution

4. The Chi-Square Distribution
When the population is normal, it is possible to construct confidence intervals for the standard deviation or variance. These confidence intervals are based on a distribution known as the chi-square distribution, denoted χ2. There are actually many different chi-square distributions, each with a different number of degrees of freedom. The figure below shows several examples of chi-square distributions.

5. The Chi-Square Distribution
There are two important characteristics of the chi-square distribution
The chi-square distributions are not symmetric. They are skewed to the right.
Values of the χ2 statistic are always greater than or equal to 0. They are never negative.

6. Critical Values
The critical values for a level 100(1−𝛼)% confidence interval are the values that contain the middle 100(1−𝛼)% of the area under the curve between them. The notation for the critical values tells how much area is to the right of the critical value. For a level 1−𝛼 confidence interval, the critical values are denoted χ2 1− 𝛼 2 and χ2 𝛼 2

7. Example – Critical Values
Find the critical values for a 95% confidence interval using the chi-square distribution with 10 degrees of freedom. Solution: The confidence level is 95%, so the critical values are the values that contain the middle 95% of the area under the curve between them. The lower critical value, denoted χ , has an area of to its right, and the upper critical value, denoted χ , has an area of to its right. Using Table A.4, the critical values are found at the intersection of the row corresponding to 10 degrees of freedom and the columns corresponding to and Thus the critical values are χ = and χ =

8. Objective 2
Construct confidence intervals for the variance and standard deviation of a normal distribution

9. Confidence Intervals for 𝜎 2 and 𝜎
Let 𝑠 2 be the sample variance from a simple random sample of size 𝑛 from a normal population A level 100(1−𝛼)% confidence interval for the population variance 𝜎 2 is 𝑛−1 𝑠 2 χ2 𝛼 2 < 𝜎 2 < 𝑛−1 𝑠 2 χ2 1− 𝛼 2 A level 100(1−𝛼)% confidence interval for the population standard deviation 𝜎 is 𝑛−1 𝑠 2 χ2 𝛼 2 < 𝜎 < 𝑛−1 𝑠 2 χ2 1− 𝛼 2 The critical values are taken from a chi-square distribution with 𝑛 – 1 degrees of freedom.

10. Example – Confidence Interval
The compressive strengths of seven concrete blocks, in pounds per square inch, are measured, with the following results Assume these values are a simple random sample from a normal population. Construct a 95% confidence interval for the population standard deviation. Solution: We first find 𝑠 2 to be 𝑠 2 = 𝑥− 𝑥 2 7−1 = Next, we find the critical values. We have 7 – 1 = 6 degrees of freedom. Since the confidence level is 95%, the critical values are χ and χ From Table A.4, we find that χ = and χ =

11. Example – Confidence Interval
Solution (continued): Next, we compute the lower and upper confidence bounds: Lower bound = 𝑛−1 𝑠 2 χ2 𝛼 2 = 7− = Upper bound = 𝑛−1 𝑠 2 χ2 1− 𝛼 2 = 7− = The 95% confidence interval for 𝜎 2 is < 𝜎 2 < 13, To find the confidence interval for 𝜎, we take the square roots. The 95% confidence interval for 𝜎 is < 𝜎 < We are 95% confident that the population variance of the strengths of the concrete blocks is between and

12. Justification For the Method
Confidence intervals for the variance of a normal distribution are based on the fact that when a sample of size 𝑛 is drawn from a normal distribution, the quantity 𝑛−1 𝑠 2 𝜎 2 follows a chi-square distribution with 𝑛 – 1 degrees of freedom. Therefore, for a proportion 1−𝛼 of all possible samples, χ2 1−𝛼 2 < 𝑛−1 𝑠 2 𝜎 2 < χ2 𝛼 2 Through algebraic manipulation, we can solve for 𝜎 2 , to obtain a level 100(1−𝛼)% confidence interval for 𝜎 2 : 𝑛−1 𝑠 2 χ2 𝛼 2 < 𝜎 2 < 𝑛−1 𝑠 2 χ2 1− 𝛼 2

13. Caution
The methods of this section apply only for samples drawn from a normal distribution. If the distribution differs even slightly from normal, these methods should not be used.

14. You Should Know…
How to find critical values of the chi-square distribution.
How to construct and interpret confidence intervals for the variance and standard deviation of a normal distribution.