Transcript Ch7-5

Section 7.5
Estimation of a Population
Variance
This section presents methods for
estimating a population variance s2
and standard deviation s.
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Best Point Estimate of
2
s
The sample variance s2 is
the best point estimate of
the population variance s2
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2
Best Point Estimate of s
The sample standard deviation s
is the best point estimate of the
population standard deviation s
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The
2

Distribution
2
(  -dist )
Pronounced “Chi-squared”
Also dependent on the number
degrees of freedom df.
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Properties of the 2 Distribution
1. The chi-square distribution is not symmetric,
unlike the z-dist and t-dist.
2. The values can be zero or positive, they are nonnegative.
3. Dependent on the Degrees of Freedom: df = n – 1
Chi-Square Distribution
Chi-Square Distribution for
df = 10 and df = 20
Use StatCrunch to Calculate values (similar to z-dist and t-dist)
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Calculating values from 2-dist
Stat → Calculators → Chi-Squared
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Calculating values from 2-dist
Enter Degrees of Freedom DF and parameters
( same procedure as with t-dist )
P(2 < 10)= 0.5595 when df = 10
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Example:
Find the 90% left and right critical values
(2L and 2R) of the 2-dist when df = 20
Need to calculate values when the left/right areas are 0.05 ( i.e. α/2 )
2L = 10.851
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2R = 31.410
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Important Note!!
The 2-distribution is used for
calculating the Confidence Interval of
the Variance σ2
Take the square-root of the values to
get the Confidence Interval of the
Standard Deviation σ
( This is why we call it 2 instead of  )
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Confidence Interval for Estimating a
Population Variance
Note: Left and Right Critical values on opposite sides
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10
Confidence Interval for Estimating a
Population Standard Deviation
Note: Left and Right Critical values on opposite sides
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Requirement for Application
The population MUST be
normally distributed to hold
(even when using large samples)
This requirement is very strict!
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Round-Off Rules for Confidence
Intervals Used to Estimate s or s 2
1. When using the original set of data, round the
confidence interval limits to one more decimal
place than used in original set of data.
2. When the original set of data is unknown and
only the summary statistics (n, x, s) are
used, round the confidence interval limits to the
same number of decimal places used for the
sample standard deviation.
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Example
Suppose the scores a test follow a normal distribution. Given
a sample of size 40 with mean 72.8 and standard deviation
4.92, find the 95% C.I. of the population standard deviation.
Direct Computation:
Chi-Squared Calculator (df = 39)
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Example
Suppose the scores a test follow a normal distribution. Given
a sample of size 40 with mean 72.8 and standard deviation
4.92, find the 95% C.I. of the population standard deviation.
Using StatCrunch
Stat → Variance → One Sample → with Summary
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Example
Suppose the scores a test follow a normal distribution. Given
a sample of size 40 with mean 72.8 and standard deviation
4.92, find the 95% C.I. of the population standard deviation.
Using StatCrunch
Sample Variance
Enter parameters, then click Next
Be sure to enter the sample variance s2 (not s)
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Example
Suppose the scores a test follow a normal distribution. Given
a sample of size 40 with mean 72.8 and standard deviation
4.92, find the 95% C.I. of the population standard deviation.
Using StatCrunch
Select Confidence Interval, enter Confidence Level,
then click Calculate
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Example
Suppose the scores a test follow a normal distribution. Given
a sample of size 40 with mean 72.8 and standard deviation
4.92, find the 95% C.I. of the population standard deviation.
Using StatCrunch
Remember:
The result is the C.I for the Variance σ2
Take the square root for Standard Deviation σ
Variance Lower Limit: LLσ2
Variance Upper Limit: ULσ2
σ2 CI = ( LLσ , ULσ ) = (16.2, 39.9)
2
σ
2
CI = ( LLσ , ULσ ) = (4.03, 6.32)
2
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Determining Sample Sizes
The procedure for finding the sample size
necessary to estimate s2 is based on Table 7-2
You just read the required sample size from an
appropriate line of the table.
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Table 7-2
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Example
We want to estimate the standard deviation s.
We want to be 95% confident that our estimate is
within 20% of the true value of s.
Assume that the population is normally distributed.
How large should the sample be?
For s 95% confident and within 20%
From Table 7-2 (see next slide), we can see that
95% confidence and an error of 20% for s
correspond to a sample of size 48.
We should obtain a sample of 48 values.
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For s 95% confident and within 20%
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