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Section 7.5 Estimation of a Population Variance

Section 7.5 Estimation of a Population Variance. This section presents methods for estimating a population variance s 2 and standard deviation s. Best Point Estimate of s 2. The sample variance s 2 is the best point estimate of the population variance s 2. Best Point Estimate of s.

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Section 7.5 Estimation of a Population Variance

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  1. Section 7.5Estimation of a Population Variance This section presents methods for estimating apopulation variances2and standard deviation s.

  2. Best Point Estimate of s2 The sample variance s2is the best point estimate of the population variances2

  3. Best Point Estimate of s Thesample standard deviation s is the best point estimateof the population standard deviations

  4. The 2 Distribution ( 2-dist ) Pronounced “Chi-squared” Also dependent on the number degrees of freedom df.

  5. Properties of the 2 Distribution The chi-square distribution is not symmetric, unlike the z-dist and t-dist. The values can be zero or positive, they are nonnegative. Dependent on the Degrees of Freedom: df = n – 1 Chi-Square Distribution for df = 10 and df = 20 Chi-Square Distribution Use StatCrunch to Calculate values (similar to z-dist and t-dist)

  6. Calculating values from 2-dist Stat → Calculators → Chi-Squared

  7. 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

  8. Example: Find the 90% left and rightcritical values (2Land 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 2R= 31.410

  9. 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 )

  10. Confidence Interval for Estimating a Population Variance Note: Left and Right Critical values on opposite sides

  11. Confidence Interval for Estimating a Population Standard Deviation Note: Left and Right Critical values on opposite sides

  12. Requirement for Application The population MUST be normally distributed to hold(even when using large samples) This requirement is very strict!

  13. Round-Off Rules for Confidence Intervals Used to Estimate  or  2 • When using the original set of data, round the confidence interval limits to one more decimal place than used in original set of data. • 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.

  14. Example Suppose the scores a test follow a normal distribution. Given a sample of size 40with 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)

  15. Example Suppose the scores a test follow a normal distribution. Given a sample of size 40with 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

  16. Example Suppose the scores a test follow a normal distribution. Given a sample of size 40with 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)

  17. Example Suppose the scores a test follow a normal distribution. Given a sample of size 40with 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

  18. Example Suppose the scores a test follow a normal distribution. Given a sample of size 40with 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 = (LLσ2, ULσ2) = (16.2, 39.9) σ CI = (LLσ2, ULσ2) = (4.03, 6.32)

  19. Determining Sample Sizes The procedure for finding the sample size necessary to estimate 2 is based on Table 7-2 You just read the required sample size from an appropriate line of the table.

  20. Table 7-2

  21. Example We want to estimate the standard deviation . We want to be 95% confident that our estimate is within 20%of the true value of . Assume that the population is normally distributed. How large should the sample be? For 95% confident andwithin 20% From Table 7-2 (see next slide), we can see that 95% confidence and an error of 20% for  correspond to a sample of size 48. We should obtain a sample of 48 values.

  22. For 95% confident andwithin 20%

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