Transcript Estimating a Population Variance

Section 7-5
Estimating a Population
Variance
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 1
Key Concept
This section we introduce the chi-square
probability distribution so that we can
construct confidence interval estimates of a
population standard deviation or variance. We
also present a method for determining the
sample size required to estimate a population
standard deviation or variance.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 2
Chi-Square Distribution
In a normally distributed population with
variance 2 assume that we randomly select
independent samples of size n and, for each
sample, compute the sample variance s2 (which
is the square of the sample standard deviation
s). The sample statistic 2 (pronounced chisquare) has a sampling distribution called the
chi-square distribution.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 3
Chi-Square Distribution
 =
2
(n – 1) s2
2
where
n = sample size
s 2 = sample variance
2 = population variance
degrees of freedom = n – 1
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 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.
As the number of degrees of freedom increases, the
distribution becomes more symmetric.
Chi-Square Distribution
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
Chi-Square Distribution for
df = 10 and df = 20
7.1 - 5
Properties of the Distribution
of the Chi-Square Statistic – cont.
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. As the number of
degrees of freedom 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 cumulative area located to the right of the
critical value.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 6
Example
A simple random sample of ten voltage levels
is obtained. Construction of a confidence
interval for the population standard deviation 
requires the left and right critical values of 2
corresponding to a confidence level of 95%
and a sample size of n = 10. Find the critical
value of 2 separating an area of 0.025 in the
left tail, and find the critical value of 2
separating an area of 0.025 in the right tail.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 7
Example
Critical Values of the Chi-Square Distribution
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 8
Example
For a sample of 10 values taken from a
normally distributed population, the chisquare statistic 2 = (n – 1)s2/2 has a 0.95
probability of falling between the chi-square
critical values of 2.700 and 19.023.
Instead of using Table A-4, technology (such as
STATDISK, Excel, and Minitab) can be used to find
critical values of 2. A major advantage of
technology is that it can be used for any number of
degrees of freedom and any confidence level, not
just the limited choices included in Table A-4.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 9
Estimators of 
2
The sample variance s2 is the best
point estimate of the population
variance 2.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 10
Estimators of 
The sample standard deviation s is a
commonly used point estimate of 
(even though it is a biased estimate).
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 11
Confidence Interval for Estimating a
Population Standard Deviation or
Variance
 = population standard deviation
s = sample standard deviation
n = number of sample values
 L2= left-tailed critical value of 2
 2 = population variance
s 2 = sample variance
E = margin of error
 = right-tailed critical value of 2
2
R
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 12
Confidence Interval for Estimating a
Population Standard Deviation or
Variance
Requirements:
1. The sample is a simple random sample.
2. The population must have normally
distributed values (even if the sample is
large).
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 13
Confidence Interval for Estimating a
Population Standard Deviation or
Variance
Confidence Interval for the Population Variance 2
n  1s

2
R
2

Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
2
n  1s



2
2
L
7.1 - 14
Confidence Interval for Estimating a
Population Standard Deviation or
Variance
Confidence Interval for the Population
Standard Deviation 
n  1s

2
R
2
 
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
n  1s

2
2
L
7.1 - 15
Procedure for Constructing a
Confidence Interval for  or  2
1. Verify that the required assumptions are satisfied.
2. Using n – 1 degrees of freedom, refer to Table
A-4 or use technology to find the critical values
2R and 2Lthat correspond to the desired
confidence level.
3. Evaluate the upper and lower confidence interval
limits using this format of the confidence interval:
n  1s

2
R
2

Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
2
n  1s



2
2
L
7.1 - 16
Procedure for Constructing a
Confidence Interval for  or  2 - cont
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.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 17
Confidence Intervals for
Comparing Data
Caution
Confidence intervals can be used informally
to compare the variation in different data
sets, but the overlapping of confidence
intervals should not be used for making
formal and final conclusions about equality
of variances or standard deviations.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 18
Example:
The proper operation of typical home
appliances requires voltage levels that do not
vary much. Listed below are ten voltage levels
(in volts) recorded in the author’s home on ten
different days. These ten values have a standard
deviation of s = 0.15 volt. Use the sample data to
construct a 95% confidence interval estimate of
the standard deviation of all voltage levels.
123.3 123.5 123.7 123.4 123.6 123.5 123.5 123.4
123.6 123.8
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 19
Example:
Requirements are satisfied: simple random
sample and normality
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 20
Example:
n = 10 so df = 10 – 1 = 9
Use table A-4 to find:
  2.700
2
L
and
  19.023
2
R
Construct the confidence interval: n = 10, s = 0.15
n  1s

2

2
R
10  10.15 
2

2
19.023
n  1s


2
2
L
10  10.15 


2

Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
2
2.700
7.1 - 21
Example:
Evaluation the preceding expression yields:
0.010645    0.075000
2
Finding the square root of each part (before
rounding), then rounding to two decimal places,
yields this 95% confidence interval estimate of
the population standard deviation:
0.10 volt    0.27 volt.
Based on this result, we have 95% confidence
that the limits of 0.10 volt and 0.27 volt contain
the true value of .
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 22
Determining Sample Sizes
The procedures for finding the sample size
necessary to estimate 2 are much more
complex than the procedures given earlier for
means and proportions. Instead of using very
complicated procedures, we will use Table 7-2.
STATDISK also provides sample sizes. With
STATDISK, select Analysis, Sample Size
Determination, and then Estimate St Dev.
Minitab, Excel, and the TI-83/84 Plus calculator
do not provide such sample sizes.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 23
Determining Sample Sizes
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 24
Example:
We want to estimate the standard deviation  of
all voltage levels in a home. We want to be 95%
confident that our estimate is within 20% of the
true value of . How large should the sample
be? Assume that the population is normally
distributed.
From Table 7-2, we can see that 95% confidence
and an error of 20% for  correspond to a
sample of size 48. We should obtain a simple
random sample of 48 voltage levels form the
population of voltage levels.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 25
Recap
In this section we have discussed:
 The chi-square distribution.
 Using Table A-4.
 Confidence intervals for the population
variance and standard deviation.
 Determining sample sizes.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved.
7.1 - 26