7 Confidence Intervals

Save this PDF as:

Size: px
Start display at page:

Transcription

1 blu49076_ch07.qxd 5/20/2003 3:15 PM Page 325 c h a p t e r 7 7 Confidence Intervals and Sample Size Outline 7 1 Introduction 7 2 Confidence Intervals for the Mean (s Known or n 30) and Sample Size 7 3 Confidence Intervals for the Mean (s Unknown and n 30) 7 4 Confidence Intervals and Sample Size for Proportions 7 5 Confidence Intervals for Variances and Standard Deviations 7 6 Summary Objectives After completing this chapter, you should be able to 1. Find the confidence interval for the mean when s is known or n Determine the minimum sample size for finding a confidence interval for the mean. 3. Find the confidence interval for the mean when s is unknown and n Find the confidence interval for a proportion. 5. Determine the minimum sample size for finding a confidence interval for a proportion. 6. Find a confidence interval for a variance and a standard deviation. 7 1

2 blu49076_ch07.qxd 5/20/2003 3:15 PM Page Chapter 7 Confidence Intervals and Sample Size Statistics Today Would You Change the Channel? A survey by the Roper Organization found that 45% of the people who were offended by a television program would change the channel, while 15% would turn off their television sets. The survey further stated that the margin of error is 3 percentage points and 4000 adults were interviewed. Several questions arise: 1. How do these estimates compare with the true population percentages? 2. What is meant by a margin of error of 3 percentage points? 3. Is the sample of 4000 large enough to represent the population of all adults who watch television in the United States? After reading this chapter, you will be able to answer these questions, since this chapter explains how statisticians can use statistics to make estimates of parameters. Source: The Associated Press. 7 1 Introduction 7 2 One aspect of inferential statistics is estimation, which is the process of estimating the value of a parameter from information obtained from a sample. For example, The Book of Odds, by Michael D. Shook and Robert L. Shook (New York: Penguin Putnam, Inc.), contains the following statements: One out of 4 Americans is currently dieting. (Calorie Control Council.)

3 blu49076_ch07.qxd 5/20/2003 3:15 PM Page 327 Section 7 2 Confidence Intervals for the Mean (s Known or n 30) and Sample Size 327 Seventy-two percent of Americans have flown on commercial airlines. ( The Bristol Meyers Report: Medicine in the Next Century. ) The average kindergarten student has seen more than 5000 hours of television. (U.S. Department of Education.) The average school nurse makes \$32,786 a year. (National Association of School Nurses.) The average amount of life insurance is \$108,000 per household with life insurance. (American Council of Life Insurance.) Since the populations from which these values were obtained are large, these values are only estimates of the true parameters and are derived from data collected from samples. The statistical procedures for estimating the population mean, proportion, variance, and standard deviation will be explained in this chapter. An important question in estimation is that of sample size. How large should the sample be in order to make an accurate estimate? This question is not easy to answer since the size of the sample depends on several factors, such as the accuracy desired and the probability of making a correct estimate. The question of sample size will be explained in this chapter also. 7 2 Confidence Intervals for the Mean (S Known or n 30) and Sample Size Objective 1. Find the confidence interval for the mean when is known or n 30. Suppose a college president wishes to estimate the average age of students attending classes this semester. The president could select a random sample of 100 students and find the average age of these students, say, 22.3 years. From the sample mean, the president could infer that the average age of all the students is 22.3 years. This type of estimate is called a point estimate. A point estimate is a specific numerical value estimate of a parameter. The best point estimate of the population mean m is the sample mean X. One might ask why other measures of central tendency, such as the median and mode, are not used to estimate the population mean. The reason is that the means of samples vary less than other statistics (such as medians and modes) when many samples are selected from the same population. Therefore, the sample mean is the best estimate of the population mean. Sample measures (i.e., statistics) are used to estimate population measures (i.e., parameters). These statistics are called estimators. As previously stated, the sample mean is a better estimator of the population mean than the sample median or sample mode. A good estimator should satisfy the three properties described now. Three Properties of a Good Estimator 1. The estimator should be an unbiased estimator. That is, the expected value or the mean of the estimates obtained from samples of a given size is equal to the parameter being estimated. 2. The estimator should be consistent. For a consistent estimator, as sample size increases, the value of the estimator approaches the value of the parameter estimated. 3. The estimator should be a relatively efficient estimator. That is, of all the statistics that can be used to estimate a parameter, the relatively efficient estimator has the smallest variance. 7 3

4 blu49076_ch07.qxd 5/20/2003 3:15 PM Page Chapter 7 Confidence Intervals and Sample Size Confidence Intervals As stated in Chapter 6, the sample mean will be, for the most part, somewhat different from the population mean due to sampling error. Therefore, one might ask a second question: How good is a point estimate? The answer is that there is no way of knowing how close the point estimate is to the population mean. This answer places some doubt on the accuracy of point estimates. For this reason, statisticians prefer another type of estimate, called an interval estimate. An interval estimate of a parameter is an interval or a range of values used to estimate the parameter. This estimate may or may not contain the value of the parameter being estimated. Historical Notes Point and interval estimates were known as long ago as the late 1700s. However, it wasn t until 1937 that a mathematician, J. Neyman, formulated practical applications for them. 7 4 In an interval estimate, the parameter is specified as being between two values. For example, an interval estimate for the average age of all students might be 26.9 m 27.7, or years. Either the interval contains the parameter or it does not. A degree of confidence (usually a percent) can be assigned before an interval estimate is made. For instance, one may wish to be 95% confident that the interval contains the true population mean. Another question then arises. Why 95%? Why not 99% or 99.5%? If one desires to be more confident, such as 99% or 99.5% confident, then the interval must be larger. For example, a 99% confidence interval for the mean age of college students might be 26.7 m 27.9, or Hence, a tradeoff occurs. To be more confident that the interval contains the true population mean, one must make the interval wider. The confidence level of an interval estimate of a parameter is the probability that the interval estimate will contain the parameter. A confidence interval is a specific interval estimate of a parameter determined by using data obtained from a sample and by using the specific confidence level of the estimate. Intervals constructed in this way are called confidence intervals. Three common confidence intervals are used: the 90%, the 95%, and the 99% confidence intervals. The algebraic derivation of the formula for determining a confidence interval for a mean will be shown later. A brief intuitive explanation will be given first. The central limit theorem states that when the sample size is large, approximately 95% of the sample means will fall within 1.96 standard errors of the population mean, that is, m 1.96 s n Now, if a specific sample mean is selected, say, X, there is a 95% probability that it falls within the range of m 1.96(s/ n). Likewise, there is a 95% probability that the interval specified by X 1.96 s n will contain m, as will be shown later. Stated another way, s X 1.96 n m X 1.96 s n

5 blu49076_ch07.qxd 5/20/2003 3:15 PM Page 329 Section 7 2 Confidence Intervals for the Mean (s Known or n 30) and Sample Size 329 Hence, one can be 95% confident that the population mean is contained within that interval when the values of the variable are normally distributed in the population. The value used for the 95% confidence interval, 1.96, is obtained from Table E in Appendix C. For a 99% confidence interval, the value 2.58 is used instead of 1.96 in the formula. This value is also obtained from Table E and is based on the standard normal distribution. Since other confidence intervals are used in statistics, the symbol z a/2 (read zee sub alpha over two ) is used in the general formula for confidence intervals. The Greek letter a (alpha) represents the total area in both tails of the standard normal distribution curve, and a/2 represents the area in each one of the tails. More will be said after Examples 7 1 and 7 2 about finding other values for z a/2. The relationship between a and the confidence level is that the stated confidence level is the percentage equivalent to the decimal value of 1 a, and vice versa. When the 95% confidence interval is to be found, a 0.05, since , or 95%. When a 0.01, then 1 a , and the 99% confidence interval is being calculated. Formula for the Confidence Interval of the Mean for a Specific A X z 2 s n m X z s 2 n For a 90% confidence interval, z a/2 1.65; for a 95% confidence interval, z a/2 1.96; and for a 99% confidence interval, z a/ The term z a/2 (s/ n) is called the maximum error of estimate. For a specific value, say, a 0.05, 95% of the sample means will fall within this error value on either side of the population mean, as previously explained. See Figure 7 1. Figure % Confidence Interval α = 0.05 α 2 = % α 2 = z α /2 ( ) µ ( ) σ zα /2 σ n n Distribution of X s The maximum error of estimate is the maximum likely difference between the point estimate of a parameter and the actual value of the parameter. A more detailed explanation of the maximum error of estimate follows Examples 7 1 and 7 2, which illustrate the computation of confidence intervals. 7 5

6 blu49076_ch07.qxd 5/20/2003 3:15 PM Page Chapter 7 Confidence Intervals and Sample Size Rounding Rule for a Confidence Interval for a Mean When you are computing a confidence interval for a population mean by using raw data, round off to one more decimal place than the number of decimal places in the original data. When you are computing a confidence interval for a population mean by using a sample mean and a standard deviation, round off to the same number of decimal places as given for the mean. Example 7 1 Example 7 2 The president of a large university wishes to estimate the average age of the students presently enrolled. From past studies, the standard deviation is known to be 2 years. A sample of 50 students is selected, and the mean is found to be 23.2 years. Find the 95% confidence interval of the population mean. Solution Since the 95% confidence interval is desired, z a/ Hence, substituting in the formula one gets X z 2 s n m X z s 2 n m m m 23.8 or years. Hence, the president can say, with 95% confidence, that the average age of the students is between 22.6 and 23.8 years, based on 50 students. A survey of 30 adults found that the mean age of a person s primary vehicle is 5.6 years. Assuming the standard deviation of the population is 0.8 year, find the 99% confidence interval of the population mean. Source: Based on information in USA TODAY. Solution m m m 5.98 or 5.2 m 6.0 (rounded) Hence, one can be 99% confident that the mean age of all primary vehicles is between 5.2 years and 6.0 years, based on 30 vehicles. 7 6 Another way of looking at a confidence interval is shown in Figure 7 2. According to the central limit theorem, approximately 95% of the sample means fall within 1.96 standard deviations of the population mean if the sample size is 30 or more or if s is known when n is less than 30 and the population is normally distributed. If it were

7 blu49076_ch07.qxd 5/20/2003 3:15 PM Page 331 Section 7 2 Confidence Intervals for the Mean (s Known or n 30) and Sample Size 331 possible to build a confidence interval about each sample mean, as was done in Examples 7 1 and 7 2 for m, 95% of these intervals would contain the population mean, as shown in Figure 7 3. Hence, one can be 95% confident that an interval built around a specific sample mean would contain the population mean. Figure % Confidence Interval for Sample Means µ 1.96 σ ( ) n µ 95% σ µ 1.96( n ) Each represents an X Figure % Confidence Intervals for Each Sample Mean µ Each represents an interval about a sample mean If one desires to be 99% confident, the confidence intervals must be enlarged so that 99 out of every 100 intervals contain the population mean. 7 7

8 blu49076_ch07.qxd 5/20/2003 3:15 PM Page Chapter 7 Confidence Intervals and Sample Size Since other confidence intervals (besides 90%, 95%, and 99%) are sometimes used in statistics, an explanation of how to find the values for z a/2 is necessary. As stated previously, the Greek letter a represents the total of the areas in both tails of the normal distribution. The value for a is found by subtracting the decimal equivalent for the desired confidence level from 1. For example, if one wanted to find the 98% confidence interval, one would change 98% to 0.98 and find a , or Then a/2 is obtained by dividing a by 2. So a/2 is 0.02/2, or Finally, z 0.01 is the z value that will give an area of 0.01 in the right tail of the standard normal distribution curve. See Figure 7 4. Figure 7 4 Finding /2 for a 98% Confidence Interval α = 0.02 α 2 = α = z α /2 0 z α /2 Once a/2 is determined, the corresponding z a/2 value can be found by using the procedure shown in Chapter 6 (see Example 6 17), which is reviewed here. To get the z a/2 value for a 98% confidence interval, subtract 0.01 from to get Next, locate the area that is closest to (in this case, ) in Table E, and then find the corresponding z value. In this example, it is See Figure 7 5. Figure 7 5 Finding z /2 for a 98% Confidence Interval z Table E The Standard Normal Distribution For confidence intervals, only the positive z value is used in the formula. When the original variable is normally distributed and a is known, the standard normal distribution can be used to find confidence intervals regardless of the size of the sample. When n 30, the distribution of means will be approximately normal even if the original distribution of the variable departs from normality. Also, if n 30 (some authors use n 30), s can be substituted for s in the formula for confidence intervals; and the standard normal distribution can be used to find confidence intervals for means, as shown in Example

9 blu49076_ch07.qxd 5/20/2003 3:16 PM Page 333 Section 7 2 Confidence Intervals for the Mean (s Known or n 30) and Sample Size 333 Example 7 3 The following data represent a sample of the assets (in millions of dollars) of 30 credit unions in southwestern Pennsylvania. Find the 90% confidence interval of the mean Source: Pittsburgh Post Gazette. Solution STEP 1 STEP 2 STEP 3 STEP 4 Find the mean and standard deviation for the data. Use the formulas shown in Chapter 3 or your calculator. The mean X The standard deviation s Find a/2. Since the 90% confidence interval is to be used, a , and Find z a/2. Subtract 0.05 from to get The corresponding z value obtained from Table E is (Note: This value is found by using the z value for an area between and A more precise z value obtained mathematically is and is sometimes used; however, 1.65 will be used in this textbook.) Substitute in the formula X z a 2 s n m X z a 2 s n (Since n 30, s is used in place of s when s is unknown.) m m m Hence, one can be 90% confident that the population mean of the assets of all credit unions is between \$6.752 million and \$ million, based on a sample of 30 credit unions. 7 9

10 blu49076_ch07.qxd 5/20/2003 3:16 PM Page Chapter 7 Confidence Intervals and Sample Size Comment to Computer and Statistical Calculator Users This chapter and subsequent chapters include examples using raw data. If you are using computer or calculator programs to find the solutions, the answers you get may vary somewhat from the ones given in the textbook. This is due to the fact that computers and calculators do not round the answers in the intermediate steps and can use 12 or more decimal places for computation. Also, they use more exact values than those given in the tables in the back of this book. These discrepancies are part and parcel of statistics. Sample Size Objective 2. Determine the minimum sample size for finding a confidence interval for the mean. Sample size determination is closely related to statistical estimation. Quite often, one asks, How large a sample is necessary to make an accurate estimate? The answer is not simple, since it depends on three things: the maximum error of estimate, the population standard deviation, and the degree of confidence. For example, how close to the true mean does one want to be (2 units, 5 units, etc.), and how confident does one wish to be (90%, 95%, 99%, etc.)? For the purpose of this chapter, it will be assumed that the population standard deviation of the variable is known or has been estimated from a previous study. The formula for sample size is derived from the maximum error of estimate formula E z 2 s n and this formula is solved for n as follows: E n z 2 s n z 2 s E Hence, n z 2 s E 2 Formula for the Minimum Sample Size Needed for an Interval Estimate of the Population Mean n z 2 s E 2 where E is the maximum error of estimate. If necessary, round the answer up to obtain a whole number. That is, if there is any fraction or decimal portion in the answer, use the next whole number for sample size n. Example 7 4 The college president asks the statistics teacher to estimate the average age of the students at their college. How large a sample is necessary? The statistics teacher would like to be 99% confident that the estimate should be accurate within 1 year. From a previous study, the standard deviation of the ages is known to be 3 years. Solution 7 10 Since a 0.01 (or ), z a/2 2.58, and E 1, substituting in the formula, one gets

11 blu49076_ch07.qxd 5/20/2003 3:16 PM Page 335 Section 7 2 Confidence Intervals for the Mean (s Known or n 30) and Sample Size 335 n z 2 s E which is rounded up to 60. Therefore, to be 99% confident that the estimate is within 1 year of the true mean age, the teacher needs a sample size of at least 60 students. Notice that when one is finding the sample size, the size of the population is irrelevant when the population is large or infinite or when sampling is done with replacement. In other cases, an adjustment is made in the formula for computing sample size. This adjustment is beyond the scope of this book. The formula for determining sample size requires the use of the population standard deviation. What, then, happens when s is unknown? In this case, an attempt is made to estimate s. One such way is to use the standard deviation s obtained from a sample taken previously as an estimate for s. The standard deviation can also be estimated by dividing the range by 4. Sometimes, interval estimates rather than point estimates are reported. For instance, one may read a statement such as On the basis of a sample of 200 families, the survey estimates that an American family of two spends an average of \$84 per week for groceries. One can be 95% confident that this estimate is accurate within \$3 of the true mean. This statement means that the 95% confidence interval of the true mean is \$84 \$3 m \$84 \$3 \$81 m \$87 The algebraic derivation of the formula for a confidence interval is shown next. As explained in Chapter 6, the sampling distribution of the mean is approximately normal when large samples (n 30) are taken from a population. Also, z X m s n Furthermore, there is a probability of 1 a that a z will have a value between z a/2 and z a/2. Hence, z 2 X m s n z 2 Using algebra, one finds z 2 s n X m z 2 s n Subtracting X from both sides and from the middle, one gets X z 2 s n m X z 2 s n Multiplying by 1, one gets X z 2 s n m X z 2 s n Reversing the inequality, one gets the formula for the confidence interval: X z 2 s n m X z 2 s n 7 11

12 blu49076_ch07.qxd 5/20/2003 3:16 PM Page Chapter 7 Confidence Intervals and Sample Size Exercises What is the difference between a point estimate and an interval estimate of a parameter? Which is better? Why? 2. What information is necessary to calculate a confidence interval? 3. What is the maximum error of estimate? 4. What is meant by the 95% confidence interval of the mean? 5. What are three properties of a good estimator? 6. What statistic best estimates m? 7. What is necessary to determine the sample size? 8. When one is determining the sample size for a confidence interval, is the size of the population relevant? 9. Find each. a. z a/2 for the 99% confidence interval b. z a/2 for the 98% confidence interval c. z a/2 for the 95% confidence interval d. z a/2 for the 90% confidence interval e. z a/2 for the 94% confidence interval 10. Find the 95% confidence interval for the mean paid attendance at the Major League All-Star games. A random sample of the paid attendances is shown. 47,596 68,751 5,838 69,831 28,843 53,107 31,391 48,829 50,706 62,892 55,105 63,974 56,674 38,362 51,549 31,938 31,851 56,088 34,906 38,359 72,086 34,009 50,850 43,801 46,127 49,926 54,960 32,785 48,321 49,671 Source: Time Almanac. 11. A sample of the reading scores of 35 fifth-graders has a mean of 82. The standard deviation of the sample is 15. a. Find the 95% confidence interval of the mean reading scores of all fifth-graders. b. Find the 99% confidence interval of the mean reading scores of all fifth-graders. c. Which interval is larger? Explain why. 12. Find the 90% confidence interval of the population mean for the incomes of western Pennsylvania credit unions. A random sample of 50 credit unions is shown. The data are in thousands of dollars Source: Pittsburgh Post Gazette. 13. A study of 40 English composition professors showed that they spent, on average, 12.6 minutes correcting a student s term paper. a. Find the 90% confidence interval of the mean time for all composition papers when s 2.5 minutes. b. If a professor stated that he spent, on average, 30 minutes correcting a term paper, what would be your reaction? 14. A study of 40 bowlers showed that their average score was 186. The standard deviation of the population is 6. a. Find the 95% confidence interval of the mean score for all bowlers. b. Find the 95% confidence interval of the mean score if a sample of 100 bowlers is used instead of a sample of 40. c. Which interval is smaller? Explain why. 15. A survey of individuals who passed the seven exams and obtained the rank of Fellow in the actuarial field finds the average salary to be \$150,000. If the standard deviation for the sample of 35 Fellows was \$15,000, construct a 95% confidence interval for all Fellows. Source: BeAnActuary.org. 16. A random sample of the number of farms (in thousands) in various states is found below. Estimate the mean number of farms per state with 90% confidence Source: N.Y. Times Almanac. 17. Find the 90% confidence interval for the mean number of local jobs for top corporations in southwestern Pennsylvania. A sample of 40 selected corporations is shown. 7 12

13 blu49076_ch07.qxd 5/20/2003 3:16 PM Page 337 Section 7 2 Confidence Intervals for the Mean (s Known or n 30) and Sample Size 337 7,685 3, ,778 7,300 3, ,370 5,400 1, ,953 3,114 2,600 2,821 6,200 3,483 8, ,000 1,650 1, , , , ,960 1,195 2, ,703 4,236 1,400 Source: Pittsburgh Tribune Review. 18. A random sample of 48 days taken at a large hospital shows that an average of 38 patients were treated in the emergency room (ER) per day. The standard deviation of the population is 4. a. Find the 99% confidence interval of the mean number of ER patients treated each day at the hospital. b. Find the 99% confidence interval of the mean number of ER patients treated each day if the standard deviation were 8 instead of 4. c. Why is the confidence interval for part b wider than the one for part a? 19. Noise levels at various area urban hospitals were measured in decibels. The mean of the noise levels in 84 corridors was 61.2 decibels, and the standard deviation was 7.9. Find the 95% confidence interval of the true mean. Source: M. Bayo, A. Garcia, and A. Garcia, Noise Levels in an Urban Hospital and Workers Subjective Responses, Archives of Environmental Health 50, no. 3, p. 249 (May June 1995). Reprinted with permission of the Helen Dwight Reid Educational Foundation. Published by Heldref Publications, 1319 Eighteenth St. N.W., Washington, D.C Copyright The growing seasons for a random sample of 35 U.S. cities were recorded, yielding a sample mean of days and a sample standard deviation of 54.2 days. Estimate the true mean population of the growing season with 95% confidence. Source: The Old Farmer s Almanac. 21. How many cities growing seasons would have to be sampled in order to estimate the true mean growing season with 95% confidence within 2 days? (Use the standard deviation from Exercise 20.) 22. In the hospital study cited in Exercise 19, the mean noise level in the 171 ward areas was 58.0 decibels, and the standard deviation was 4.8. Find the 90% confidence interval of the true mean. Source: M. Bayo, A. Garcia, and A. Garcia, Noise Levels in an Urban Hospital and Workers Subjective Responses, Archives of Environmental Health 50, no. 3, p. 249 (May June 1995). Reprinted with permission of the Helen Dwight Reid Educational Foundation. Published by Heldref Publications, 1319 Eighteenth St. N.W., Washington, D.C Copyright An insurance company is trying to estimate the average number of sick days that full-time food service workers use per year. A pilot study found the standard deviation to be 2.5 days. How large a sample must be selected if the company wants to be 95% confident of getting an interval that contains the true mean with a maximum error of 1 day? 24. A restaurant owner wishes to find the 99% confidence interval of the true mean cost of a dry martini. How large should the sample be if she wishes to be accurate within \$0.10? A previous study showed that the standard deviation of the price was \$ A health care professional wishes to estimate the birth weights of infants. How large a sample must she select if she desires to be 90% confident that the true mean is within 6 ounces of the sample mean? The standard deviation of the birth weights is known to be 8 ounces. Technology Step by Step MINITAB Step by Step Finding a z Confidence Interval for the Mean For Example 7 3, find the 90% confidence interval estimate for the mean amount of assets for credit unions in southwestern Pennsylvania. 1. Maximize the worksheet then enter the data into C1 of a MINITAB worksheet. If sigma is known, skip to step Calculate the standard deviation for the sample. It will be used as an estimate for sigma. 7 13

14 blu49076_ch07.qxd 5/20/2003 3:16 PM Page Chapter 7 Confidence Intervals and Sample Size a) Select Calc>Column statistics. b) Click the option for Standard deviation. c) Enter C1 Assets for the Input variable and s for Store in:. 3. Select Stat>Basic Statistics>1-Sample Z. 4. Select C1 Assets for the Samples in Columns. 5. Click in the box for Standard Deviation and enter s. Leave the box for Test mean empty. 6. Click the [Options] button. In the dialog box make sure the Confidence Level is 90 and the Alternative is not equal. 7. Optional: Click [Graphs], then select Boxplot of data. The boxplot of these data would clearly show the outliers! 8. Click [OK] twice. The results will be displayed in the session window. One-Sample Z: Assets The assumed sigma = Variable N Mean StDev SE Mean 90% CI Assets (6.7646, ) TI-83 Plus Step by Step 7 14 Finding a z Confidence Interval for the Mean (Data) 1. Enter the data into L Press STAT and move the cursor to TESTS. 3. Press 7 for ZInterval. 4. Move the cursor to Data and press ENTER. 5. Type in the appropriate values. 6. Move the cursor to Calculate and press ENTER. Example TI7 1 This is Example 7 3 from the text. Find the 90% confidence interval for the population mean, given the data values The population standard deviation s is unknown. Since the sample size is n 30, one can use the sample standard deviation s as an approximation for s. After the data values are entered in L 1 (step 1 above), press STAT, move the cursor to CALC, press 1 for 1-Var Stats, then press

15 blu49076_ch07.qxd 5/20/2003 3:16 PM Page 339 Section 7 2 Confidence Intervals for the Mean (s Known or n 30) and Sample Size 339 ENTER. The sample standard deviation of will be one of the statistics listed. Then continue with step 2. At step 5 on the line for s, press VARS for variables, press 5 for Statistics, press 3 for S x. The 90% confidence interval is m The difference between these limits and the ones in Example 7 3 is due to rounding. Finding a z Confidence Interval for the Mean (Statistics) 1. Press STAT and move the cursor to TESTS. 2. Press 7 for ZInterval. 3. Move the cursor to Stats and press ENTER. 4. Type in the appropriate values. 5. Move the cursor to Calculate and press ENTER. Example TI7 2 This is Example 7 1 from the text. Find the 95% confidence interval for the population mean, given s 2, X 23.2, and n 50. The 95% confidence interval is 22.6 m Excel Step by Step Finding a z Confidence Interval for the Mean Excel has a procedure to produce the maximum error of the estimate. But it does not produce confidence intervals. However, you may determine confidence intervals for the mean by using the Mega-Stat Add-in available on your CD and Online Learning Center. If you have not installed this add-in, do so by following the instructions on page 24. Find the 95% confidence interval when s 11, using this sample: From the toolbar, select Mega-Stat>Confidence Intervals/Sample Size. 2. In the dialog box, select the Confidence interval mean tab. 3. Enter for the mean of the data, and then select z. Enter 11 for the standard deviation and 30 for n, the sample size. 4. Either type in or scroll to 95% for the confidence level, and then click [OK]. The result of the procedure is shown next. 7 15

16 blu49076_ch07.qxd 5/20/2003 3:16 PM Page Chapter 7 Confidence Intervals and Sample Size Confidence interval - mean 95% confidence level mean 11 std. dev. 30 n z half-width upper confidence limit lower confidence limit 7 3 Confidence Intervals for the Mean (S Unknown and n < 30) Objective 3. Find the confidence interval for the mean when is unknown and n 30. Historical Notes The t distribution was formulated in 1908 by an Irish brewing employee named W. S. Gosset. Gosset was involved in researching new methods of manufacturing ale. Because brewing employees were not allowed to publish results, Gosset published his finding using the pseudonym Student; hence, the t distribution is sometimes called Student s t distribution. When s is known and the variable is normally distributed or when s is unknown and n 30, the standard normal distribution is used to find confidence intervals for the mean. However, in many situations, the population standard deviation is not known and the sample size is less than 30. In such situations, the standard deviation from the sample can be used in place of the population standard deviation for confidence intervals. But a somewhat different distribution, called the t distribution, must be used when the sample size is less than 30 and the variable is normally or approximately normally distributed. Some important characteristics of the t distribution are described now. Characteristics of the t Distribution The t distribution shares some characteristics of the normal distribution and differs from it in others. The t distribution is similar to the standard normal distribution in these ways. 1. It is bell-shaped. 2. It is symmetric about the mean. 3. The mean, median, and mode are equal to 0 and are located at the center of the distribution. 4. The curve never touches the x axis. The t distribution differs from the standard normal distribution in the following ways. 1. The variance is greater than The t distribution is actually a family of curves based on the concept of degrees of freedom, which is related to sample size. 3. As the sample size increases, the t distribution approaches the standard normal distribution. See Figure 7 6. Figure 7 6 The t Family of Curves z t for d.f. = 20 t for d.f. = Many statistical distributions use the concept of degrees of freedom, and the formulas for finding the degrees of freedom vary for different statistical tests. The degrees

17 blu49076_ch07.qxd 5/20/2003 3:16 PM Page 341 Section 7 3 Confidence Intervals for the Mean (s Unknown or n 30) 341 of freedom are the number of values that are free to vary after a sample statistic has been computed, and they tell the researcher which specific curve to use when a distribution consists of a family of curves. For example, if the mean of 5 values is 10, then 4 of the 5 values are free to vary. But once 4 values are selected, the fifth value must be a specific number to get a sum of 50, since Hence, the degrees of freedom are 5 1 4, and this value tells the researcher which t curve to use. The symbol d.f. will be used for degrees of freedom. The degrees of freedom for a confidence interval for the mean are found by subtracting 1 from the sample size. That is, d.f. n 1. Note: For some statistical tests used later in this book, the degrees of freedom are not equal to n 1. The formula for finding a confidence interval about the mean by using the t distribution is given now. Formula for a Specific Confidence Interval for the Mean When S Is Unknown and n < 30 X t 2 s n m X t s 2 n The degrees of freedom are n 1. The values for t a/2 are found in Table F in Appendix C. The top row of Table F, labeled Confidence Intervals, is used to get these values. The other two rows, labeled One Tail and Two Tails, will be explained in Chapter 8 and should not be used here. Example 7 5 shows how to find the value in Table F for t a/2. Example 7 5 Find the t a/2 value for a 95% confidence interval when the sample size is 22. Solution The d.f. 22 1, or 21. Find 21 in the left column and 95% in the row labeled Confidence intervals. The intersection where the two meet gives the value for t a/2, which is See Figure 7 7. Figure 7 7 Finding t /2 for Example 7 5 Confidence Intervals Table F The t Distribution 50% 80% 90% 95% 98% 99% d.f One Tail α Two Tails α z a b c d 7 17

18 blu49076_ch07.qxd 5/20/2003 3:16 PM Page Chapter 7 Confidence Intervals and Sample Size Note: At the bottom of Table F where d.f., the z a/2 values can be found for specific confidence intervals. The reason is that as the degrees of freedom increase, the t distribution approaches the standard normal distribution. Examples 7 6 and 7 7 show how to find the confidence interval when one is using the t distribution. Example Historical Note Gosset derived the t distribution by selecting small random samples of measurements taken from a population of incarcerated criminals. For the measures he used the lengths of one of their fingers. Example 7 7 Ten randomly selected automobiles were stopped, and the tread depth of the right front tire was measured. The mean was 0.32 inch, and the standard deviation was 0.08 inch. Find the 95% confidence interval of the mean depth. Assume that the variable is approximately normally distributed. Solution Since s is unknown and s must replace it, the t distribution (Table F) must be used for 95% confidence interval. Hence, with 9 degrees of freedom, t a/ The 95% confidence interval of the population mean is found by substituting in the formula X t 2 s n m X t s 2 n Hence, m m m 0.38 Therefore, one can be 95% confident that the population mean tread depth of all right front tires is between 0.26 and 0.38 inch based on a sample of 10 tires. The data represent a sample of the number of home fires started by candles for the past several years. (Data are from the National Fire Protection Association.) Find the 99% confidence interval for the mean number of home fires started by candles each year Solution STEP 1 Find the mean and standard deviation for the data. Use the formulas in Chapter 3 or your calculator. The mean X The standard deviation s STEP 2 Find t a/2 in Table F. Use the 99% confidence interval with d.f. 6. It is STEP 3 Substitute in the formula and solve. X t 2 s n m X t s 2 n m m m

19 blu49076_ch07.qxd 5/20/2003 3:16 PM Page 343 Section 7 3 Confidence Intervals for the Mean (s Unknown or n 30) 343 One can be 99% confident that the population mean of home fires started by candles each year is between and , based on a sample of home fires occurring over a period of 7 years. Students sometimes have difficulty deciding whether to use z a/2 or t a/2 values when finding confidence intervals for the mean. As stated previously, when s is known, z a/2 values can be used no matter what the sample size is, as long as the variable is normally distributed or n 30. When s is unknown and n 30, s can be used in the formula and z a/2 values can be used. Finally, when s is unknown and n 30, s is used in the formula and t a/2 values are used, as long as the variable is approximately normally distributed. These rules are summarized in Figure 7 8. Figure 7 8 When to Use the z or t Distribution Is σ known? Yes Use z α/2 values no matter what the sample size is.* No Is n > 30? Yes Use z α/2 values and s in place of σ in the formula. No Use t α/2 values and s in the formula.** *Variable must be normally distributed when n < 30. **Variable must be approximately normally distributed. It should be pointed out that some statisticians have a different point of view. They use z a/2 values when s is known and t a/2 values when s is unknown. In these circumstances, a t table that contains t values for sample sizes greater than or equal to 30 would be needed. The procedure shown in Figure 7 8 is the one used throughout this textbook. Exercises What are the properties of the t distribution? 2. What is meant by degrees of freedom? 3. When should the t distribution be used to find a confidence interval for the mean? 4. (ans) Find the values for each. a. t a/2 and n 18 for the 99% confidence interval for the mean b. t a/2 and n 23 for the 95% confidence interval for the mean c. t a/2 and n 15 for the 98% confidence interval for the mean d. t a/2 and n 10 for the 90% confidence interval for the mean e. t a/2 and n 20 for the 95% confidence interval for the mean For Exercises 5 through 20, assume that all variables are approximately normally distributed. 5. The average hemoglobin reading for a sample of 20 teachers was 16 grams per 100 milliliters, with a sample standard deviation of 2 grams. Find the 99% confidence interval of the true mean. 6. A sample of 17 states had these cigarette taxes (in cents): Find a 98% confidence interval for the cigarette tax in all 50 states. Source: Federation of Tax Administrators. 7 19

20 blu49076_ch07.qxd 5/20/2003 3:16 PM Page Chapter 7 Confidence Intervals and Sample Size 7. A state representative wishes to estimate the mean number of women representatives per state legislature. A random sample of 17 states is selected, and the number of women representatives is shown. Based on the sample, what is the point estimate of the mean? Find the 90% confidence interval of the mean population. (Note: The population mean is actually 31.72, or about 32.) Compare this value to the point estimate and the confidence interval. There is something unusual about the data. Describe it and state how it would affect the confidence interval A random sample of the number of barrels (in millions) of oil produced per day by world oilproducing countries is listed here. Estimate the mean oil production with 95% confidence Source: N.Y. Times Almanac. 9. A sample of six adult elephants had an average weight of 12,200 pounds, with a sample standard deviation of 200 pounds. Find the 95% confidence interval of the true mean. 10. The daily salaries of substitute teachers for eight local school districts is shown. What is the point estimate for the mean? Find the 90% confidence interval of the mean for the salaries of substitute teachers in the region Source: Pittsburgh Tribune Review. 11. A recent study of 28 city residents showed that the mean of the time they had lived at their present address was 9.3 years. The standard deviation of the sample was 2 years. Find the 90% confidence interval of the true mean. 12. An automobile shop manager timed six employees and found that the average time it took them to change a water pump was 18 minutes. The standard deviation of the sample was 3 minutes. Find the 99% confidence interval of the true mean. 13. A recent study of 25 students showed that they spent an average of \$18.53 for gasoline per week. The standard deviation of the sample was \$3.00. Find the 95% confidence interval of the true mean. 14. For a group of 10 men subjected to a stress situation, the mean number of heartbeats per minute was 126, and the standard deviation was 4. Find the 95% confidence interval of the true mean. 15. For the stress test described in Exercise 14, six women had an average heart rate of 115 beats per minute. The standard deviation of the sample was 6 beats. Find the 95% confidence interval of the true mean for the women. 16. For a sample of 24 operating rooms taken in the hospital study mentioned in Exercise 19 in Section 7 2, the mean noise level was 41.6 decibels, and the standard deviation was 7.5. Find the 95% confidence interval of the true mean of the noise levels in the operating rooms. Source: M. Bayo, A. Garcia, and A. Garcia, Noise Levels in an Urban Hospital and Workers Subjective Responses, Archives of Environmental Health 50, no. 3, p. 249 (May June 1995). Reprinted with permission of the Helen Dwight Reid Educational Foundation. Published by Heldref Publications, 1319 Eighteenth St. N.W., Washington, D.C Copyright The number of grams of carbohydrates in a 12- ounce serving of a regular soft drink is listed here for a random sample of sodas. Estimate the mean number of carbohydrates in all brands of soda with 95% confidence Source: The Doctor s Pocket Calorie, Fat, and Carbohydrate Counter. 18. For a group of 20 students taking a final exam, the mean heart rate was 96 beats per minute, and the standard deviation was 5. Find the 95% confidence interval of the true mean. 19. The average yearly income for 28 married couples living in city C is \$58,219. The standard deviation of the sample is \$56. Find the 95% confidence interval of the true mean. 20. The number of unhealthy days based on the AQI (Air Quality Index) for a random sample of metropolitan areas is shown. Construct a 98% confidence interval based on the data Source: N.Y. Times Almanac. Extending the Concepts 21. A one-sided confidence interval can be found for a mean by using 7 20 s m X t n or m X t s n

21 blu49076_ch07.qxd 5/20/2003 3:16 PM Page 345 Section 7 3 Confidence Intervals for the Mean (s Unknown or n 30) 345 where t a is the value found under the row labeled One Tail. Find two one-sided 95% confidence intervals of the population mean for the data shown and interpret the answers. The data represent the daily revenues in dollars from 20 parking meters in a small municipality Technology Step by Step MINITAB Step by Step Find a t Interval for the Mean For Example 7 7, find the 99% confidence interval for the mean number of home fires started by candles each year. 1. Type the data into C1 of a MINITAB worksheet. Name the column HomeFires. 2. Select Stat>Basic Statistics>1-Sample t. 3. Double-click C1 HomeFires for the Samples in Columns. 4. Click on [Options] and be sure the Confidence Level is 99 and the Alternative is not equal. 5. Click [OK] twice. 6. Check for normality: a) Select Graph>Probability Plot, then Single. b) Select C1 HomeFires for the variable. The normal plot is concave, a skewed distribution. In the session window you will see the results. The 99% confidence interval estimate for m is between and The sample size, mean, standard deviation, and standard error of the mean are also shown. However, this small sample appears to have a nonnormal population. The interval is less likely to contain the true mean. 7 21

22 blu49076_ch07.qxd 5/20/2003 3:16 PM Page Chapter 7 Confidence Intervals and Sample Size One-Sample T: HomeFires Variable N Mean StDev SE Mean 99% CI HomeFires ( , ) TI-83 Plus Step by Step Finding a t Confidence Interval for the Mean (Data) 1. Enter the data into L Press STAT and move the cursor to TESTS. 3. Press 8 for TInterval. 4. Move the cursor to Data and press ENTER. 5. Type in the appropriate values. 6. Move the cursor to Calculate and press ENTER. Finding a t Confidence Interval for the Mean (Statistics) 1. Press STAT and move the cursor to TESTS. 2. Press 8 for TInterval. 3. Move the cursor to Stats and press ENTER. 4. Type in the appropriate values. 5. Move the cursor to Calculate and press ENTER. Excel Step by Step Finding a t Confidence Interval for the Mean Excel has a procedure to produce the maximum error of the estimate. But it does not produce confidence intervals. However, you may determine confidence intervals for the mean by using the Mega-Stat Add-in available on your CD and Online Learning Center. If you have not installed this add-in, do so by following the instructions on page 24. Find the 95% confidence interval, using this sample: From the toolbar, select Mega-Stat>Confidence Intervals/Sample Size. 2. In the dialog box, select the Confidence interval mean tab. 3. Enter for the mean of the data, and then select t. Enter 87.9 for the standard deviation and 10 for n, the sample size. 4. Either type in or scroll to 95% for the confidence level, and then click [OK]. The result of the procedure will show the output in a new chart as indicated below. Confidence interval - mean 95% confidence level mean 87.9 std. dev. 10 n t (df 9) half-width upper confidence limit lower confidence limit 7 4 Confidence Intervals and Sample Size for Proportions 7 22 A USA TODAY Snapshots feature stated that 12% of the pleasure boats in the United States were named Serenity. The parameter 12% is called a proportion. It means that of all the pleasure boats in the United States, 12 out of every 100 are named Serenity. A proportion represents a part of a whole. It can be expressed as a fraction, decimal, or percentage. In this case, 12% 0.12 or. Proportions can also represent 12 3 probabilities

23 blu49076_ch07.qxd 5/20/2003 3:16 PM Page 347 Section 7 4 Confidence Intervals and Sample Size for Proportions 347 Objective 4. Find the confidence interval for a proportion. In this case, if a pleasure boat is selected at random, the probability that it is called Serenity is Proportions can be obtained from samples or populations. The following symbols will be used. Symbols Used in Proportion Notation p symbol for population proportion pˆ (read p hat ) symbol for sample proportion For a sample proportion, pˆ X n and qˆ n X n or 1 pˆ where X number of sample units that possess the characteristics of interest and n sample size. For example, in a study, 200 people were asked if they were satisfied with their job or profession; 162 said that they were. In this case, n 200, X 162, and pˆ X/n 162/ It can be said that for this sample, 0.81, or 81%, of those surveyed were satisfied with their job or profession. The sample proportion is pˆ The proportion of people who did not respond favorably when asked if they were satisfied with their job or profession constituted qˆ, where qˆ (n X)/n. For this survey, qˆ ( )/200 38/200, or 0.19, or 19%. When pˆ and qˆ are given in decimals or fractions, pˆ qˆ 1. When pˆ and qˆ are given in percentages, pˆ qˆ 100%. It follows, then, that qˆ 1 pˆ, or pˆ 1 qˆ, when pˆ and qˆ are in decimal or fraction form. For the sample survey on job satisfaction, qˆ can also be found by using qˆ 1 pˆ, or Similar reasoning applies to population proportions; that is, p 1 q, q 1 p, and p q 1, when p and q are expressed in decimal or fraction form. When p and q are expressed as percentages, p q 100%, p 100% q, and q 100% p. Example 7 8 In a recent survey of 150 households, 54 had central air conditioning. Find pˆ and qˆ, where pˆ is the proportion of households that have central air conditioning. Solution Since X 54 and n 150, pˆ X n % 150 qˆ n X % n One can also find qˆ by using the formula qˆ 1 pˆ. In this case, qˆ As with means, the statistician, given the sample proportion, tries to estimate the population proportion. Point and interval estimates for a population proportion can be made by using the sample proportion. For a point estimate of p (the population proportion), pˆ (the sample proportion) is used. On the basis of the three properties of a good 7 23

8 6 X 2 Test for a Variance or Standard Deviation

Section 8 6 x 2 Test for a Variance or Standard Deviation 437 This test uses the P-value method. Therefore, it is not necessary to enter a significance level. 1. Select MegaStat>Hypothesis Tests>Proportion

9 Testing the Difference

blu49076_ch09.qxd 5/1/2003 8:19 AM Page 431 c h a p t e r 9 9 Testing the Difference Between Two Means, Two Variances, and Two Proportions Outline 9 1 Introduction 9 2 Testing the Difference Between Two

5/31/2013. Chapter 8 Hypothesis Testing. Hypothesis Testing. Hypothesis Testing. Outline. Objectives. Objectives

C H 8A P T E R Outline 8 1 Steps in Traditional Method 8 2 z Test for a Mean 8 3 t Test for a Mean 8 4 z Test for a Proportion 8 6 Confidence Intervals and Copyright 2013 The McGraw Hill Companies, Inc.

MINITAB ASSISTANT WHITE PAPER

MINITAB ASSISTANT WHITE PAPER This paper explains the research conducted by Minitab statisticians to develop the methods and data checks used in the Assistant in Minitab 17 Statistical Software. One-Way

Minitab Guide. This packet contains: A Friendly Guide to Minitab. Minitab Step-By-Step

Minitab Guide This packet contains: A Friendly Guide to Minitab An introduction to Minitab; including basic Minitab functions, how to create sets of data, and how to create and edit graphs of different

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. A) ±1.88 B) ±1.645 C) ±1.96 D) ±2.

Ch. 6 Confidence Intervals 6.1 Confidence Intervals for the Mean (Large Samples) 1 Find a Critical Value 1) Find the critical value zc that corresponds to a 94% confidence level. A) ±1.88 B) ±1.645 C)

6 3 The Standard Normal Distribution

290 Chapter 6 The Normal Distribution Figure 6 5 Areas Under a Normal Distribution Curve 34.13% 34.13% 2.28% 13.59% 13.59% 2.28% 3 2 1 + 1 + 2 + 3 About 68% About 95% About 99.7% 6 3 The Distribution Since

5/31/2013. 6.1 Normal Distributions. Normal Distributions. Chapter 6. Distribution. The Normal Distribution. Outline. Objectives.

The Normal Distribution C H 6A P T E R The Normal Distribution Outline 6 1 6 2 Applications of the Normal Distribution 6 3 The Central Limit Theorem 6 4 The Normal Approximation to the Binomial Distribution

5.1 Identifying the Target Parameter

University of California, Davis Department of Statistics Summer Session II Statistics 13 August 20, 2012 Date of latest update: August 20 Lecture 5: Estimation with Confidence intervals 5.1 Identifying

Data Analysis Tools. Tools for Summarizing Data

Data Analysis Tools This section of the notes is meant to introduce you to many of the tools that are provided by Excel under the Tools/Data Analysis menu item. If your computer does not have that tool

Chapter 1: Exploring Data

Chapter 1: Exploring Data Chapter 1 Review 1. As part of survey of college students a researcher is interested in the variable class standing. She records a 1 if the student is a freshman, a 2 if the student

MBA 611 STATISTICS AND QUANTITATIVE METHODS

MBA 611 STATISTICS AND QUANTITATIVE METHODS Part I. Review of Basic Statistics (Chapters 1-11) A. Introduction (Chapter 1) Uncertainty: Decisions are often based on incomplete information from uncertain

Standard Deviation Estimator

CSS.com Chapter 905 Standard Deviation Estimator Introduction Even though it is not of primary interest, an estimate of the standard deviation (SD) is needed when calculating the power or sample size of

Confidence Intervals for the Difference Between Two Means

Chapter 47 Confidence Intervals for the Difference Between Two Means Introduction This procedure calculates the sample size necessary to achieve a specified distance from the difference in sample means

CONFIDENCE INTERVALS I

CONFIDENCE INTERVALS I ESTIMATION: the sample mean Gx is an estimate of the population mean µ point of sampling is to obtain estimates of population values Example: for 55 students in Section 105, 45 of

Chapter 7 Section 7.1: Inference for the Mean of a Population

Chapter 7 Section 7.1: Inference for the Mean of a Population Now let s look at a similar situation Take an SRS of size n Normal Population : N(, ). Both and are unknown parameters. Unlike what we used

6.4 Normal Distribution

Contents 6.4 Normal Distribution....................... 381 6.4.1 Characteristics of the Normal Distribution....... 381 6.4.2 The Standardized Normal Distribution......... 385 6.4.3 Meaning of Areas under

Final Exam Practice Problem Answers The following data set consists of data gathered from 77 popular breakfast cereals. The variables in the data set are as follows: Brand: The brand name of the cereal

Exercise 1.12 (Pg. 22-23)

Individuals: The objects that are described by a set of data. They may be people, animals, things, etc. (Also referred to as Cases or Records) Variables: The characteristics recorded about each individual.

Calculate and interpret confidence intervals for one population average and one population proportion.

Chapter 8 Confidence Intervals 8.1 Confidence Intervals 1 8.1.1 Student Learning Objectives By the end of this chapter, the student should be able to: Calculate and interpret confidence intervals for one

Module 2 Probability and Statistics

Module 2 Probability and Statistics BASIC CONCEPTS Multiple Choice Identify the choice that best completes the statement or answers the question. 1. The standard deviation of a standard normal distribution

TI-Inspire manual 1. Instructions. Ti-Inspire for statistics. General Introduction

TI-Inspire manual 1 General Introduction Instructions Ti-Inspire for statistics TI-Inspire manual 2 TI-Inspire manual 3 Press the On, Off button to go to Home page TI-Inspire manual 4 Use the to navigate

Calculating P-Values. Parkland College. Isela Guerra Parkland College. Recommended Citation

Parkland College A with Honors Projects Honors Program 2014 Calculating P-Values Isela Guerra Parkland College Recommended Citation Guerra, Isela, "Calculating P-Values" (2014). A with Honors Projects.

32 Measures of Central Tendency and Dispersion

32 Measures of Central Tendency and Dispersion In this section we discuss two important aspects of data which are its center and its spread. The mean, median, and the mode are measures of central tendency

Estimating and Finding Confidence Intervals

. Activity 7 Estimating and Finding Confidence Intervals Topic 33 (40) Estimating A Normal Population Mean μ (σ Known) A random sample of size 10 from a population of heights that has a normal distribution

Chapter 7. Estimates and Sample Size

Chapter 7. Estimates and Sample Size Chapter Problem: How do we interpret a poll about global warming? Pew Research Center Poll: From what you ve read and heard, is there a solid evidence that the average

13.2 Measures of Central Tendency

13.2 Measures of Central Tendency Measures of Central Tendency For a given set of numbers, it may be desirable to have a single number to serve as a kind of representative value around which all the numbers

Hypothesis Testing. Bluman Chapter 8

CHAPTER 8 Learning Objectives C H A P T E R E I G H T Hypothesis Testing 1 Outline 8-1 Steps in Traditional Method 8-2 z Test for a Mean 8-3 t Test for a Mean 8-4 z Test for a Proportion 8-5 2 Test for

The Chi-Square Distributions

MATH 183 The Chi-Square Distributions Dr. Neal, WKU The chi-square distributions can be used in statistics to analyze the standard deviation " of a normally distributed measurement and to test the goodness

Let m denote the margin of error. Then

S:105 Statistical Methods and Computing Sample size for confidence intervals with σ known t Intervals Lecture 13 Mar. 6, 009 Kate Cowles 374 SH, 335-077 kcowles@stat.uiowa.edu 1 The margin of error The

An interval estimate (confidence interval) is an interval, or range of values, used to estimate a population parameter. For example 0.476<p<0.

Lecture #7 Chapter 7: Estimates and sample sizes In this chapter, we will learn an important technique of statistical inference to use sample statistics to estimate the value of an unknown population parameter.

4. Continuous Random Variables, the Pareto and Normal Distributions

4. Continuous Random Variables, the Pareto and Normal Distributions A continuous random variable X can take any value in a given range (e.g. height, weight, age). The distribution of a continuous random

Chapter 8. Hypothesis Testing

Chapter 8 Hypothesis Testing Hypothesis In statistics, a hypothesis is a claim or statement about a property of a population. A hypothesis test (or test of significance) is a standard procedure for testing

find confidence interval for a population mean when the population standard deviation is KNOWN Understand the new distribution the t-distribution

Section 8.3 1 Estimating a Population Mean Topics find confidence interval for a population mean when the population standard deviation is KNOWN find confidence interval for a population mean when the

Chapter 23. Inferences for Regression

Chapter 23. Inferences for Regression Topics covered in this chapter: Simple Linear Regression Simple Linear Regression Example 23.1: Crying and IQ The Problem: Infants who cry easily may be more easily

Sociology 6Z03 Topic 15: Statistical Inference for Means

Sociology 6Z03 Topic 15: Statistical Inference for Means John Fox McMaster University Fall 2016 John Fox (McMaster University) Soc 6Z03: Statistical Inference for Means Fall 2016 1 / 41 Outline: Statistical

EXCEL Tutorial: How to use EXCEL for Graphs and Calculations.

EXCEL Tutorial: How to use EXCEL for Graphs and Calculations. Excel is powerful tool and can make your life easier if you are proficient in using it. You will need to use Excel to complete most of your

Statistical Data analysis With Excel For HSMG.632 students

1 Statistical Data analysis With Excel For HSMG.632 students Dialog Boxes Descriptive Statistics with Excel To find a single descriptive value of a data set such as mean, median, mode or the standard deviation,

Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition Online Learning Centre Technology Step-by-Step - Excel Microsoft Excel is a spreadsheet software application

Chapter 7 Section 1 Homework Set A

Chapter 7 Section 1 Homework Set A 7.15 Finding the critical value t *. What critical value t * from Table D (use software, go to the web and type t distribution applet) should be used to calculate the

The Chi-Square Test. STAT E-50 Introduction to Statistics

STAT -50 Introduction to Statistics The Chi-Square Test The Chi-square test is a nonparametric test that is used to compare experimental results with theoretical models. That is, we will be comparing observed

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

STT315 Practice Ch 5-7 MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Solve the problem. 1) The length of time a traffic signal stays green (nicknamed

LAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING

LAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING In this lab you will explore the concept of a confidence interval and hypothesis testing through a simulation problem in engineering setting.

Chapter 2: Exploring Data with Graphs and Numerical Summaries. Graphical Measures- Graphs are used to describe the shape of a data set.

Page 1 of 16 Chapter 2: Exploring Data with Graphs and Numerical Summaries Graphical Measures- Graphs are used to describe the shape of a data set. Section 1: Types of Variables In general, variable can

3 3 Measures of Variation

Section 3 3 Measures of Variation 115 3 3 Measures of Variation In statistics, to describe the data set accurately, statisticians must know more than the measures of central tendency. Consider Example

Use Measures of Central Tendency and Dispersion. Measures of Central Tendency

13.6 Use Measures of Central Tendency and Dispersion Before You analyzed surveys and samples. Now You will compare measures of central tendency and dispersion. Why? So you can analyze and compare data,

Lecture Notes Module 1

Lecture Notes Module 1 Study Populations A study population is a clearly defined collection of people, animals, plants, or objects. In psychological research, a study population usually consists of a specific

KSTAT MINI-MANUAL. Decision Sciences 434 Kellogg Graduate School of Management

KSTAT MINI-MANUAL Decision Sciences 434 Kellogg Graduate School of Management Kstat is a set of macros added to Excel and it will enable you to do the statistics required for this course very easily. To

Standard Deviation Calculator

CSS.com Chapter 35 Standard Deviation Calculator Introduction The is a tool to calculate the standard deviation from the data, the standard error, the range, percentiles, the COV, confidence limits, or

MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

Final Exam Review MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) A researcher for an airline interviews all of the passengers on five randomly

CALCULATIONS & STATISTICS

CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 1-5 scale to 0-100 scores When you look at your report, you will notice that the scores are reported on a 0-100 scale, even though respondents

Chapter 2. Objectives. Tabulate Qualitative Data. Frequency Table. Descriptive Statistics: Organizing, Displaying and Summarizing Data.

Objectives Chapter Descriptive Statistics: Organizing, Displaying and Summarizing Data Student should be able to Organize data Tabulate data into frequency/relative frequency tables Display data graphically

Practice problems for Homework 12 - confidence intervals and hypothesis testing. Open the Homework Assignment 12 and solve the problems.

Practice problems for Homework 1 - confidence intervals and hypothesis testing. Read sections 10..3 and 10.3 of the text. Solve the practice problems below. Open the Homework Assignment 1 and solve the

Review. March 21, 2011. 155S7.1 2_3 Estimating a Population Proportion. Chapter 7 Estimates and Sample Sizes. Test 2 (Chapters 4, 5, & 6) Results

MAT 155 Statistical Analysis Dr. Claude Moore Cape Fear Community College Chapter 7 Estimates and Sample Sizes 7 1 Review and Preview 7 2 Estimating a Population Proportion 7 3 Estimating a Population

MATH 103/GRACEY PRACTICE EXAM/CHAPTERS 2-3. MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.

MATH 3/GRACEY PRACTICE EXAM/CHAPTERS 2-3 Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Provide an appropriate response. 1) The frequency distribution

Chapter 3: Data Description Numerical Methods

Chapter 3: Data Description Numerical Methods Learning Objectives Upon successful completion of Chapter 3, you will be able to: Summarize data using measures of central tendency, such as the mean, median,

CHAPTER 3 CENTRAL TENDENCY ANALYSES

CHAPTER 3 CENTRAL TENDENCY ANALYSES The next concept in the sequential statistical steps approach is calculating measures of central tendency. Measures of central tendency represent some of the most simple

p1^ = 0.18 p2^ = 0.12 A) 0.150 B) 0.387 C) 0.300 D) 0.188 3) n 1 = 570 n 2 = 1992 x 1 = 143 x 2 = 550 A) 0.270 B) 0.541 C) 0.520 D) 0.

Practice for chapter 9 and 10 Disclaimer: the actual exam does not mirror this. This is meant for practicing questions only. The actual exam in not multiple choice. Find the number of successes x suggested

Chapter 8: Hypothesis Testing for One Population Mean, Variance, and Proportion

Chapter 8: Hypothesis Testing for One Population Mean, Variance, and Proportion Learning Objectives Upon successful completion of Chapter 8, you will be able to: Understand terms. State the null and alternative

Ch. 3.1 # 3, 4, 7, 30, 31, 32

Math Elementary Statistics: A Brief Version, 5/e Bluman Ch. 3. # 3, 4,, 30, 3, 3 Find (a) the mean, (b) the median, (c) the mode, and (d) the midrange. 3) High Temperatures The reported high temperatures

The Normal Distribution

Chapter 6 The Normal Distribution 6.1 The Normal Distribution 1 6.1.1 Student Learning Objectives By the end of this chapter, the student should be able to: Recognize the normal probability distribution

Stats for Strategy Fall 2012 First-Discussion Handout: Stats Using Calculators and MINITAB

Stats for Strategy Fall 2012 First-Discussion Handout: Stats Using Calculators and MINITAB DIRECTIONS: Welcome! Your TA will help you apply your Calculator and MINITAB to review Business Stats, and will

Statistical Inference and t-tests

1 Statistical Inference and t-tests Objectives Evaluate the difference between a sample mean and a target value using a one-sample t-test. Evaluate the difference between a sample mean and a target value

Chapter 7 - Practice Problems 1

Chapter 7 - Practice Problems 1 SHORT ANSWER. Write the word or phrase that best completes each statement or answers the question. Provide an appropriate response. 1) Define a point estimate. What is the

Chi Square Tests. Chapter 10. 10.1 Introduction

Contents 10 Chi Square Tests 703 10.1 Introduction............................ 703 10.2 The Chi Square Distribution.................. 704 10.3 Goodness of Fit Test....................... 709 10.4 Chi Square

Statistics 104: Section 6!

Page 1 Statistics 104: Section 6! TF: Deirdre (say: Dear-dra) Bloome Email: dbloome@fas.harvard.edu Section Times Thursday 2pm-3pm in SC 109, Thursday 5pm-6pm in SC 705 Office Hours: Thursday 6pm-7pm SC

Statistics 151 Practice Midterm 1 Mike Kowalski

Statistics 151 Practice Midterm 1 Mike Kowalski Statistics 151 Practice Midterm 1 Multiple Choice (50 minutes) Instructions: 1. This is a closed book exam. 2. You may use the STAT 151 formula sheets and

t Tests in Excel The Excel Statistical Master By Mark Harmon Copyright 2011 Mark Harmon

t-tests in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. mark@excelmasterseries.com www.excelmasterseries.com

Recitation, Week 3: Basic Descriptive Statistics and Measures of Central Tendency:

Recitation, Week 3: Basic Descriptive Statistics and Measures of Central Tendency: 1. What does Healey mean by data reduction? a. Data reduction involves using a few numbers to summarize the distribution

Name: Date: Use the following to answer questions 3-4:

Name: Date: 1. Determine whether each of the following statements is true or false. A) The margin of error for a 95% confidence interval for the mean increases as the sample size increases. B) The margin

( ) ( ) Central Tendency. Central Tendency

1 Central Tendency CENTRAL TENDENCY: A statistical measure that identifies a single score that is most typical or representative of the entire group Usually, a value that reflects the middle of the distribution

! x sum of the entries

3.1 Measures of Central Tendency (Page 1 of 16) 3.1 Measures of Central Tendency Mean, Median and Mode! x sum of the entries a. mean, x = = n number of entries Example 1 Find the mean of 26, 18, 12, 31,

Exploratory data analysis (Chapter 2) Fall 2011

Exploratory data analysis (Chapter 2) Fall 2011 Data Examples Example 1: Survey Data 1 Data collected from a Stat 371 class in Fall 2005 2 They answered questions about their: gender, major, year in school,

Chapter 7 Appendix. Inference for Distributions with Excel, JMP, Minitab, SPSS, CrunchIt!, R, and TI-83/-84 Calculators

Chapter 7 Appendix Inference for Distributions with Excel, JMP, Minitab, SPSS, CrunchIt!, R, and TI-83/-84 Calculators Inference for the Mean of a Population Excel t Confidence Interval for Mean Confidence

Using Excel for inferential statistics

FACT SHEET Using Excel for inferential statistics Introduction When you collect data, you expect a certain amount of variation, just caused by chance. A wide variety of statistical tests can be applied

Classify the data as either discrete or continuous. 2) An athlete runs 100 meters in 10.5 seconds. 2) A) Discrete B) Continuous

Chapter 2 Overview Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Classify as categorical or qualitative data. 1) A survey of autos parked in

SECTION 2-1: OVERVIEW SECTION 2-2: FREQUENCY DISTRIBUTIONS

SECTION 2-1: OVERVIEW Chapter 2 Describing, Exploring and Comparing Data 19 In this chapter, we will use the capabilities of Excel to help us look more carefully at sets of data. We can do this by re-organizing

Review #2. Statistics

Review #2 Statistics Find the mean of the given probability distribution. 1) x P(x) 0 0.19 1 0.37 2 0.16 3 0.26 4 0.02 A) 1.64 B) 1.45 C) 1.55 D) 1.74 2) The number of golf balls ordered by customers of

Chapter Study Guide. Chapter 11 Confidence Intervals and Hypothesis Testing for Means

OPRE504 Chapter Study Guide Chapter 11 Confidence Intervals and Hypothesis Testing for Means I. Calculate Probability for A Sample Mean When Population σ Is Known 1. First of all, we need to find out the

CHAPTER 9 HYPOTHESIS TESTING

CHAPTER 9 HYPOTHESIS TESTING The TI-83 Plus and TI-84 Plus fully support hypothesis testing. Use the key, then highlight TESTS. The options used in Chapter 9 are given on the two screens. TESTING A SINGLE

Descriptive Statistics

Y520 Robert S Michael Goal: Learn to calculate indicators and construct graphs that summarize and describe a large quantity of values. Using the textbook readings and other resources listed on the web

How to Conduct a Hypothesis Test

How to Conduct a Hypothesis Test The idea of hypothesis testing is relatively straightforward. In various studies we observe certain events. We must ask, is the event due to chance alone, or is there some

Confidence Intervals for Cp

Chapter 296 Confidence Intervals for Cp Introduction This routine calculates the sample size needed to obtain a specified width of a Cp confidence interval at a stated confidence level. Cp is a process

Confidence Intervals

Section 6.1 75 Confidence Intervals Section 6.1 C H A P T E R 6 4 Example 4 (pg. 284) Constructing a Confidence Interval Enter the data from Example 1 on pg. 280 into L1. In this example, n > 0, so the

STT315 Chapter 4 Random Variables & Probability Distributions KM. Chapter 4.5, 6, 8 Probability Distributions for Continuous Random Variables

Chapter 4.5, 6, 8 Probability Distributions for Continuous Random Variables Discrete vs. continuous random variables Examples of continuous distributions o Uniform o Exponential o Normal Recall: A random

3: Summary Statistics

3: Summary Statistics Notation Let s start by introducing some notation. Consider the following small data set: 4 5 30 50 8 7 4 5 The symbol n represents the sample size (n = 0). The capital letter X denotes

10-3 Measures of Central Tendency and Variation

10-3 Measures of Central Tendency and Variation So far, we have discussed some graphical methods of data description. Now, we will investigate how statements of central tendency and variation can be used.

Table 2-1. Sucrose concentration (% fresh wt.) of 100 sugar beet roots. Beet No. % Sucrose. Beet No.

Chapter 2. DATA EXPLORATION AND SUMMARIZATION 2.1 Frequency Distributions Commonly, people refer to a population as the number of individuals in a city or county, for example, all the people in California.

When σ Is Known: Recall the Mystery Mean Activity where x bar = 240.79 and we have an SRS of size 16

8.3 ESTIMATING A POPULATION MEAN When σ Is Known: Recall the Mystery Mean Activity where x bar = 240.79 and we have an SRS of size 16 Task was to estimate the mean when we know that the situation is Normal

Recall this chart that showed how most of our course would be organized:

Chapter 4 One-Way ANOVA Recall this chart that showed how most of our course would be organized: Explanatory Variable(s) Response Variable Methods Categorical Categorical Contingency Tables Categorical

Describing Data. We find the position of the central observation using the formula: position number =

HOSP 1207 (Business Stats) Learning Centre Describing Data This worksheet focuses on describing data through measuring its central tendency and variability. These measurements will give us an idea of what

Practice#1(chapter1,2) Name

Practice#1(chapter1,2) Name Solve the problem. 1) The average age of the students in a statistics class is 22 years. Does this statement describe descriptive or inferential statistics? A) inferential statistics

Probability Distributions

CHAPTER 5 Probability Distributions CHAPTER OUTLINE 5.1 Probability Distribution of a Discrete Random Variable 5.2 Mean and Standard Deviation of a Probability Distribution 5.3 The Binomial Distribution

NCSS Statistical Software

Chapter 06 Introduction This procedure provides several reports for the comparison of two distributions, including confidence intervals for the difference in means, two-sample t-tests, the z-test, the

Engineering Problem Solving and Excel. EGN 1006 Introduction to Engineering

Engineering Problem Solving and Excel EGN 1006 Introduction to Engineering Mathematical Solution Procedures Commonly Used in Engineering Analysis Data Analysis Techniques (Statistics) Curve Fitting techniques

CHAPTER 7 INTRODUCTION TO SAMPLING DISTRIBUTIONS

CHAPTER 7 INTRODUCTION TO SAMPLING DISTRIBUTIONS CENTRAL LIMIT THEOREM (SECTION 7.2 OF UNDERSTANDABLE STATISTICS) The Central Limit Theorem says that if x is a random variable with any distribution having

STATS8: Introduction to Biostatistics. Data Exploration. Babak Shahbaba Department of Statistics, UCI

STATS8: Introduction to Biostatistics Data Exploration Babak Shahbaba Department of Statistics, UCI Introduction After clearly defining the scientific problem, selecting a set of representative members

Week 3&4: Z tables and the Sampling Distribution of X

Week 3&4: Z tables and the Sampling Distribution of X 2 / 36 The Standard Normal Distribution, or Z Distribution, is the distribution of a random variable, Z N(0, 1 2 ). The distribution of any other normal

Two Related Samples t Test

Two Related Samples t Test In this example 1 students saw five pictures of attractive people and five pictures of unattractive people. For each picture, the students rated the friendliness of the person

SAMPLING DISTRIBUTIONS

0009T_c07_308-352.qd 06/03/03 20:44 Page 308 7Chapter SAMPLING DISTRIBUTIONS 7.1 Population and Sampling Distributions 7.2 Sampling and Nonsampling Errors 7.3 Mean and Standard Deviation of 7.4 Shape of