1 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Estimating a Population Mean: σ Known 7-3, pg 355

2 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 7.3: Estimation of a population mean µ (σ is known ) In this section we cover methods for estimating a population mean. In addition to knowing the values of the sample data or statistics, we must also know the value of the population standard deviation, .

3 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Point Estimate of the Population Mean The sample mean x is the best point estimate of the population mean µ.

4 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Confidence Interval for Estimating a Population Mean (with  Known)  = population mean  = population standard deviation = sample mean n = number of sample values E = margin of error z  /2 = z score separating an area of a/2 in the right tail of the standard normal distribution

5 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Requirements to check: 1. The value of the population standard deviation  is known. 2. Either or both of these conditions is satisfied: The population is normally distributed or n > 30. ( Just like in the Central Limit Theorem.)

6 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Confidence Interval for Estimating a Population Mean (with  Known)

7 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Definition The two values x – E and x + E are called confidence interval limits.

8 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 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 mean. Round-Off Rule for Confidence Intervals Used to Estimate µ

9 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Press STAT and select TESTS Scroll down to ZInterval press ENTER choose Data or Stats. For Stats: Type in s: (known st. deviation) x: (sample mean) n: (sample size) C-Level: (confidence level) Press on Calculate Read the confidence interval (…..,..…) Confidence Intervals by TI-83/84 _

10 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Confidence Intervals by Excel

11 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Finding a Sample Size for Estimating a Population Mean (z  /2 )   n = E 2 σ = population standard deviation E = desired margin of error z α/2 = z score separating an area of  /2 in the right tail of the standard normal distribution

12 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Round-Off Rule for Sample Size n If the computed sample size n is not a whole number, round the value of n up to the next larger whole number.

13 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Example Using Excel:

14 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Estimating a Population Mean: σ Not Known 7-4, pg 355

15 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 7.4: Estimation of a population mean µ (σ is not known ) This section presents methods for estimating a population mean when the population standard deviation s is not known.

16 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. The sample mean x is still the best point estimate of the population mean µ. Sample Mean _

17 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Construction of a confidence intervals for mean µ (σ is not known ) With σ unknown, we use the Student t distribution instead of normal distribution. It involves a new feature: number of degrees of freedom

18 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. degrees of freedom = n – 1 in this section. Definition The number of degrees of freedom for a collection of sample data is the number of sample values that can vary after certain restrictions have been imposed on all data values. The degree of freedom is often abbreviated df.

19 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Margin of Error E for Estimate of µ (With σ Not Known) Formula 7-6 where t α/2 has n – 1 degrees of freedom. n s E = t α/2 Table A-3 lists values for t α/2 t  /2 = critical t value separating an area of  /2 in the right tail of the t distribution

20 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. where E = t α /2 n s x – E < µ < x + E t α/ 2 found in Table A-3 Confidence Interval for the Estimate of μ (With σ Not Known) df = n – 1

21 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Important Properties of the Student t Distribution 1.The Student t distribution is different for different sample sizes (see the following slide, for the cases n = 3 and n = 12). 2.The Student t distribution has the same general symmetric bell shape as the standard normal distribution but it reflects the greater variability (with wider distributions) that is expected with small samples 3.The Student t distribution has a mean of t = 0 (just as the standard normal distribution has a mean of z = 0). 4.The standard deviation of the Student t distribution varies with the sample size and is greater than 1 (unlike the standard normal distribution, which has a σ = 1). 5.As the sample size n gets larger, the Student t distribution gets closer to the normal distribution.

22 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Student t Distributions for n = 3 and n = 12 Figure 7-5

23 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Choosing the Appropriate Distribution Use the normal (z) distribution  known and normally distributed population or  known and n > 30 Use t distribution  not known and normally distributed population or  not known and n > 30 Methods of Chapter 7 do not apply Population is not normally distributed and n ≤ 30

24 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Press STAT and select TESTS Scroll down to TInterval press ENTER choose Data or Stats. For Stats: Type in x: (sample mean) S x : (sample st. deviation) n: (number of trials) C-Level: (confidence level) Press on Calculate Read the confidence interval (…..,..…) Confidence Intervals by TI-83/84 _

25 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Press STAT and select TESTS Scroll down to TInterval press ENTER choose Data or Stats. For Data: Type in List: L 1 (or L 2 or L 3 ) (specify the list containing your data) Freq: 1 (leave it) C-Level: (confidence level) Press on Calculate Read the confidence interval (…..,..…) Confidence Intervals by TI-83/84

26 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Confidence Intervals by Excel Example 2, page 358, first calculate margin of error

27 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Confidence Intervals by Excel Example 2, page 358, first calculate margin of error

28 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Point estimate of µ : x = (upper confidence limit) + (lower confidence limit) 2 Margin of Error: E = (upper confidence limit) – (lower confidence limit) 2 Finding the Point Estimate and E from a Confidence Interval

29 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 7-5 Estimating a Population Variance This section covers the estimation of a population variance  2 and standard deviation 

30 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Estimator of  2 The sample variance s 2 is the best point estimate of the population variance  2.

31 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Estimator of  The sample standard deviation s is a commonly used point estimate of .

32 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Construction of confidence intervals for  2 We use the chi-square distribution, denoted by Greek character  2 (pronounced chi-square).

33 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Properties of the Chi-Square Distribution 1. The chi-square distribution is not symmetric, unlike the normal and Student t distributions. Chi-Square Distribution Chi-Square Distribution for df = 10 and df = 20 degrees of freedom = n – 1

34 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 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 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. Properties of the Chi-Square Distribution

35 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Example A 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 in the left tail, and find the critical value of  2 separating an area of in the right tail.

36 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Example Critical Values of the Chi-Square Distribution

37 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Confidence Interval for Estimating a Population Variance

38 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Confidence Interval for Estimating a Population Standard Deviation

39 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Requirement: The population must have normally distributed values (even if the sample is large) This requirement is very strict

40 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 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. Round-Off Rule for Confidence Intervals Used to Estimate  or  2

41 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 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.

42 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Determining Sample Sizes

43 Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 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 . 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 sample of 48 values.