1. Estimating a Population Variance
Section 6-5
Estimating a Population Variance
Created by Erin Hodgess, Houston, Texas

2. Assumptions 1. The sample is a simple random sample.
2. The population must have normally distributed values (even if the sample is large).
The requirement of a normal distribution is critical with this test.
Use of nonnormal distributions can lead to gross errors.
Use the methods discussed in Section 5-7, Determining Normality, to verify a normal distribution before proceeding with this procedure.
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3. Chi-Square Distribution
(n – 1) s 2
 2 =
2
Formula 6-7
where
n = sample size
s 2 = sample variance
2 = population variance
One place where students tend to make errors with this formula is switching the values for s and . Careful reading of the problem should eliminate this difficulty.
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4. Properties of the Distribution of the Chi-Square Statistic
1. The chi-square distribution is not symmetric, unlike the normal and Student t distributions.
Figure 6-9 Chi-Square Distribution for
df = 10 and df = 20
As the number of degrees of freedom increases, the
distribution becomes more symmetric. (continued)
Figure 6-8 Chi-Square Distribution
Different degrees of freedom produce different chi-square distributions.
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5. Properties of the Distribution of the Chi-Square Statistic
(continued)
2. The values of chi-square can be zero or positive, but they cannot be negative.
3. The chi-square distribution is different for each number of degrees of freedom, which is df = n – 1 in this section. As the number increases, the chi- square distribution approaches a normal distribution.
In Table A-4, each critical value of 2 corresponds to an area given in the top row of the table, and that area represents the total region located to the right of the critical value.
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6. Example: Find the critical values of 2 that determine critical regions containing an area of in each tail. Assume that the relevant sample size is 10 so that the number of degrees of freedom is 10 – 1, or 9.
 = 0.05
/2 = 0.025
1 /2 = 0.975
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7. Critical Values: Table A-4
Areas to the right of each tail
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8. Estimators of 2
The sample variance s is the best point estimate of the population variance 2 . 2
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9. Confidence Interval for the Population Variance 2
(n – 1)s 2
(n – 1)s 2
2    2 2
Right-tail CV R L
Remind students that we have started with the variance which is the square of the standard deviation. Therefore, taking the square root of the variance formula will produce the standard deviation formula.
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10. Confidence Interval for the Population Variance 2
(n – 1)s 2
(n – 1)s 2
2    2 2
Right-tail CV R L
Left-tail CV
Remind students that we have started with the variance which is the square of the standard deviation. Therefore, taking the square root of the variance formula will produce the standard deviation formula.
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11. Confidence Interval for the Population Variance 2
(n – 1)s 2
(n – 1)s 2
2    2 2
Right-tail CV R L
Left-tail CV
Confidence Interval for the Population Standard Deviation 
(n – 1)s 2  2
Remind students that we have started with the variance which is the square of the standard deviation. Therefore, taking the square root of the variance formula will produce the standard deviation formula.
(n – 1)s 2 
 2 R L
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12. Procedure for Constructing a Confidence Interval for  or 2
1. Verify that the required assumptions are met.
2. Using n – 1 degrees of freedom, refer to Table A-4 and find the critical values 2R and 2Lthat corresponds to the desired confidence level.
3. Evaluate the upper and lower confidence interval limits using this format of the confidence interval:
2 
(n – 1)s 2  2 R L
continued
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13. Procedure for Constructing a Confidence Interval for  or 2
(continued)
4. If a confidence interval estimate of  is desired, take the square root of the upper and lower confidence interval limits and change 2 to .
5. Round the resulting confidence level limits. If using the original set of data to construct a confidence interval, round the confidence interval limits to one more decimal place than is used for the original set of data. If using the sample standard deviation or variance, round the confidence interval limits to the same number of decimals places.
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14. Example: A study found the body temperatures of 106 healthy adults
Example: A study found the body temperatures of 106 healthy adults. The sample mean was 98.2 degrees and the sample standard deviation was 0.62 degrees. Find the 95% confidence interval for .
n = 106
x = 98.2o
s = 0.62o
 = 0.05
/2 = 0.025
1 –/2 = 0.975
2R = , 2L =
(106 – 1)(0.62)2 < 2 < (106 – 1)(0.62)2
0.31 < 2 < 0.54
0.56 <  < 0.74
We are 95% confident that the limits of 0.56°F and 0.74°F contain the true value of . We are 95% confident that the standard deviation of body temperatures of all healthy people is between 0.56°F and 0.74°F.
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15. Determining Sample Size
Instead of presenting the much more complex procedures for determining sample size necessary to estimate the population variance, this table will be used.
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16. Example: We want to estimate , the standard deviation off all body temperatures. We want to be 95% confident that our estimate is within 10% of the true value of . How large should the sample be? Assume that the population is normally distributed.
From Table 6-2, we can see that 95% confidence and an error of 10% for  correspond to a sample of size 191.
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