1. Chapter 8 Estimation ©

2. Estimator and Estimate estimator estimate An estimator of a population parameter is a random variable that depends on the sample information and whose value provides approximations to this unknown parameter. A specific value of that random variable is called an estimate.

3. Point Estimator and Point Estimate Let  represent a population parameter (such as the population mean  or the population proportion  ). A point estimator,, of a population parameter, , is a function of the sample information that yields a single number called a point estimate. For example, the sample mean,, is a point estimator of the population mean , and the value that assumes for a given set of data is called the point estimate.

4. Unbiasedness The point estimator is said to be an unbiased estimator of the parameter  if the expected value, or mean, of the sampling distribution of is  ; that is,

5. Probability Density Functions for unbiased and Biased Estimators (Figure 8.1)

6. Bias Let be an estimator of . The bias in is defined as the difference between its mean and  ; that is It follows that the bias of an unbiased estimator is 0.

7. Most Efficient Estimator and Relative Efficiency most efficient estimatorminimum variance unbiased estimator Suppose there are several unbiased estimators of . Then the unbiased estimator with the smallest variance is said to be the most efficient estimator or to be the minimum variance unbiased estimator of . Let and be two unbiased estimators of , based on the same number of sample observations. Then, a) is said to be more efficient than if b) The relative efficiency of with respect to is the ratio of their variances; that is,

8. Point Estimators of Selected Population Parameters (Table 8.1) Population Parameter Point Estimator Properties Mean,  X Unbiased, Most Efficient (assuming normality) Mean,  XmXm Unbiased (assuming normality), but not most efficient Proportion,  p Unbiased, Most Efficient Variance,  2 s2s2 Unbiased, Most Efficient (assuming normality)

9. Confidence Interval Estimator confidence interval estimator confidence interval estimate A confidence interval estimator for a population parameter  is a rule for determining (based on sample information) a range, or interval that is likely to include the parameter. The corresponding estimate is called a confidence interval estimate.

10. Confidence Interval and Confidence Level confidence interval confidence level Let  be an unknown parameter. Suppose that on the basis of sample information, random variables A and B are found such that P(A <  < B) = 1 - , where  is any number between 0 and 1. If specific sample values of A and B are a and b, then the interval from a to b is called a 100(1 -  )% confidence interval of . The quantity of (1 -  ) is called the confidence level of the interval. If the population were repeatedly sampled a very large number of times, the true value of the parameter  would be contained in 100(1 -  )% of intervals calculated this way. The confidence interval calculated in this manner is written as a <  < b with 100(1 -  )% confidence.

11. P(-1.96 < Z < 1.96) = 0.95, where Z is a Standard Normal Variable (Figure 8.3) 0.95 = P(-1.96 < Z < 1.96) -1.96 1.96 0. 025

12. Notation Let Z  /2 be the number for which where the random variable Z follows a standard normal distribution.

13. Selected Values Z  /2 from the Standard Normal Distribution Table (Table 8.2)  0.010.020.050.10 Z  /2 2.582.331.961.645 Confidence Level 99%98%95%90%

14. Confidence Intervals for the Mean of a Population that is Normally Distributed: Population Variance Known confidence interval for the population mean with known variance Consider a random sample of n observations from a normal distribution with mean  and variance  2. If the sample mean is X, then a 100(1 -  )% confidence interval for the population mean with known variance is given by or equivalently, where the margin of error (also called the sampling error, the bound, or the interval half width) is given by

15. Basic Terminology for Confidence Interval for a Population Mean with Known Population Variance (Table 8.3) TermsSymbolTo Obtain: Standard Error of the Mean Z Value (also called the Reliability Factor) Use Standard Normal Distribution Table Margin of Error Lower Confidence Limit Upper Confidence Limit Width (width is twice the bound)

16. Student’s t Distribution Student’s t distribution Given a random sample of n observations, with mean X and standard deviation s, from a normally distributed population with mean , the variable t follows the Student’s t distribution with (n - 1) degrees of freedom and is given by

17. Notation A random variable having the Student’s t distribution with v degrees of freedom will be denoted t v. The t v,  /2 is defined as the number for which

18. Confidence Intervals for the Mean of a Normal Population: Population Variance Unknown confidence interval for the population mean, variance unknown Suppose there is a random sample of n observations from a normal distribution with mean  and unknown variance. If the sample mean and standard deviation are, respectively, X and s, then a 100(1 -  )% confidence interval for the population mean, variance unknown, is given by or equivalently, margin of error where the margin of error, the sampling error, or bound, B, is given by and t n-1,  /2 is the number for which The random variable t n-1 has a Student’s t distribution with v=(n-1) degrees of freedom.

19. Confidence Intervals for Population Proportion (Large Samples) confidence interval for the population proportion Let p denote the observed proportion of “successes” in a random sample of n observations from a population with a proportion  of successes. Then, if n is large enough that (n)(  )(1-  )>9, then a 100(1 -  )% confidence interval for the population proportion is given by or equivalently, margin of error where the margin of error, the sampling error, or bound, B, is given by and Z  /2, is the number for which a standard normal variable Z satisfies

20. Notation A random variable having the chi-square distribution with v = n-1 degrees of freedom will be denoted by  2 v or simply  2 n-1. Define as  2 n-1,  the number for which

21. The Chi-Square Distribution (Figure 8.17)  1 -   2 n-1,  0

22. The Chi-Square Distribution for n – 1 and (1-  )% Confidence Level (Figure 8.18)  /2 1 -   2 n-1,  /2  /2  2 n-1,1-  /2

23. Confidence Intervals for the Variance of a Normal Population confidence interval for the population variance Suppose there is a random sample of n observations from a normally distributed population with variance  2. If the observed variance is s 2, then a 100(1 -  )% confidence interval for the population variance is given by where  2 n-1,  /2 is the number for which and  2 n-1,1 -  /2 is the number for which And the random variable  2 n-1 follows a chi-square distribution with (n – 1) degrees of freedom.

24. Confidence Intervals for Two Means: Matched Pairs confidence interval for the difference between means Suppose that there is a random sample of n matched pairs of observations from a normal distributions with means  X and  Y. That is, x 1, x 2,..., x n denotes the values of the observations from the population with mean  X ; and y 1, y 2,..., y n the matched sampled values from the population with mean  Y. Let d and s d denote the observed sample mean and standard deviation for the n differences d i = x i – y i. If the population distribution of the differences is assumed to be normal, then a 100(1 -  )% confidence interval for the difference between means (  d =  X -  Y ) is given by or equivalently,

25. Confidence Intervals for Two Means: Matched Pairs (continued) margin of error Where the margin of error, the sampling error or the bound, B, is given by And t n-1,  /2 is the number for which The random variable t n – 1, has a Student’s t distribution with (n – 1) degrees of freedom.

26. Confidence Intervals for Difference Between Means: Independent Samples (Normal Distributions and Known Population Variances) Suppose that there are two independent random samples of n x and n y observations from normally distributed populations with means  X and  Y and variances  2 x and  2 y. If the observed sample means are X and Y, then a 100(1 -  )% confidence interval for (  X -  Y ) is given by or equivalently, margin of error where the margin of error is given by

27. Confidence Intervals for Two Means: Unknown Population Variances that are Assumed to be Equal normally Suppose that there are two independent random samples with n x and n y observations from normally distributed populations with means  X and  Y and a common, but unknown population variance. If the observed sample means are X and Y, and the observed sample variances are s 2 X and s 2 Y, then a 100(1 -  )% confidence interval for (  X -  Y ) is given by or equivalently, margin of error where the margin of error is given by

28. Confidence Intervals for Two Means: Unknown Population Variances that are Assumed to be Equal (continued) pooled sample variance The pooled sample variance, s 2 p, is given by is the number for which The random variable, T, is approximately a Student’s t distribution with n X + n Y –2 degrees of freedom and T is given by,

29. Confidence Intervals for Two Means: Unknown Population Variances, Assumed Not Equal independent random samples normally Suppose that there are two independent random samples of n x and n y observations from normally distributed populations with means  X and  Y and it is assumed that the population variances are not equal. If the observed sample means and variances are X, Y, and s 2 X, s 2 Y, then a 100(1 -  )% confidence interval for (  X -  Y ) is given by margin of error where the margin of error is given by

30. Confidence Intervals for Two Means: Unknown Population Variances, Assumed Not Equal (continued) The degrees of freedom, v, is given by If the sample sizes are equal, then the degrees of freedom reduces to

31. Confidence Intervals for the Difference Between Two Population Proportions (Large Samples) confidence interval for the difference between population proportions Let p X, denote the observed proportion of successes in a random sample of n X observations from a population with proportion  X successes, and let p Y denote the proportion of successes observed in an independent random sample from a population with proportion  Y successes. Then, if the sample sizes are large (generally at least forty observations in each sample), a 100(1 -  )% confidence interval for the difference between population proportions (  X -  Y ) is given by Where the margin of error is

32. Sample Size for the Mean of a Normally Distributed Population with Known Population Variance sample size Suppose that a random sample from a normally distributed population with known variance  2 is selected. Then a 100(1 -  )% confidence interval for the population mean extends a distance B (sometimes called the bound, sampling error, or the margin of error) on each side of the sample mean, if the sample size, n, is

33. Sample Size for Population Proportion sample size Suppose that a random sample is selected from a population. Then a 100(1 -  )% confidence interval for the population proportion, extending a distance of at most B on each side of the sample proportion, can be guaranteed if the sample size, n, is

34. Key Words 4Bias 4Bound 4Confidence interval: 4For mean, known variance 4For mean, unknown variance 4For proportion 4For two means, matched 4For two means, variances equal 4For two means, variances not equal 4For variance 4 Confidence Level 4 Estimate 4 Estimator 4 Interval Half Width 4 Lower Confidence Limit (LCL) 4 Margin of Error 4 Minimum Variance Unbiased Estimator 4 Most Efficient Estimator 4 Point Estimate 4 Point Estimator

35. Key Words (continued) 4 Relative Efficiency 4 Reliability Factor 4 Sample Size for Mean, Known Variance 4 Sample Size for Proportion 4 Sampling Error 4 Student’s t 4 Unbiased Estimator 4 Upper Confidence Limit (UCL) 4 Width