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Chapter 5 Normal Probability Distributions 1 Chapter Outline • 5.1 Introduction to Normal Distributions and the Standard Normal Distribution • 5.2 Normal Distributions: Finding Probabilities • 5.3 Normal Distributions: Finding Values • 5.4 Sampling Distributions and the Central Limit Theorem • 5.5 Normal Approximations to Binomial Distributions 2 Section 5.1 Introduction to Normal Distributions 3 Section 5.1 Objectives • Interpret graphs of normal probability distributions • Find areas under the standard normal curve 4 Properties of a Normal Distribution Continuous random variable • Has an infinite number of possible values that can be represented by an interval on the number line. Hours spent studying in a day 0 3 6 9 12 15 18 21 24 The time spent studying can be any number between 0 and 24. Continuous probability distribution • The probability distribution of a continuous random variable. 5 Properties of Normal Distributions Normal distribution • A continuous probability distribution for a random variable, x. • The most important continuous probability distribution in statistics. • The graph of a normal distribution is called the normal curve. x 6 Properties of Normal Distributions 1. The mean, median, and mode are equal. 2. The normal curve is bell-shaped and symmetric about the mean. 3. The total area under the curve is equal to one. 4. The normal curve approaches, but never touches the x-axis as it extends farther and farther away from the mean. Total area = 1 μ x 7 Properties of Normal Distributions 5. Between μ – σ and μ + σ (in the center of the curve), the graph curves downward. The graph curves upward to the left of μ – σ and to the right of μ + σ. The points at which the curve changes from curving upward to curving downward are called the inflection points. Inflection points μ 3σ μ 2σ μσ μ μ+σ μ + 2σ μ + 3σ x 8 Means and Standard Deviations • A normal distribution can have any mean and any positive standard deviation. • The mean gives the location of the line of symmetry. • The standard deviation describes the spread of the data. μ = 3.5 σ = 1.5 μ = 3.5 σ = 0.7 μ = 1.5 σ = 0.7 9 Example: Understanding Mean and Standard Deviation 1. Which curve has the greater mean? Solution: Curve A has the greater mean (The line of symmetry of curve A occurs at x = 15. The line of symmetry of curve B occurs at x = 12.) 10 Example: Understanding Mean and Standard Deviation 2. Which curve has the greater standard deviation? Solution: Curve B has the greater standard deviation (Curve B is more spread out than curve A.) 11 Example: Interpreting Graphs The heights of fully grown white oak trees are normally distributed. The curve represents the distribution. What is the mean height of a fully grown white oak tree? Estimate the standard deviation. Solution: μ = 90 (A normal curve is symmetric about the mean) σ = 3.5 (The inflection points are one standard deviation away from the mean) 12 The Standard Normal Distribution Standard normal distribution • A normal distribution with a mean of 0 and a standard deviation of 1. Area = 1 3 2 1 z 0 1 2 3 • Any x-value can be transformed into a z-score by using the formula Value - Mean x- z Standard deviation 13 The Standard Normal Distribution • If each data value of a normally distributed random variable x is transformed into a z-score, the result will be the standard normal distribution. Normal Distribution z x x- Standard Normal Distribution 1 0 z • Use the Standard Normal Table to find the cumulative area under the standard normal curve. 14 Properties of the Standard Normal Distribution 1. The cumulative area is close to 0 for z-scores close to z = 3.49. 2. The cumulative area increases as the z-scores increase. Area is close to 0 z = 3.49 z 3 2 1 0 1 2 3 15 Properties of the Standard Normal Distribution 3. The cumulative area for z = 0 is 0.5000. 4. The cumulative area is close to 1 for z-scores close to z = 3.49. Area is close to 1 z 3 2 1 0 1 z=0 Area is 0.5000 2 3 z = 3.49 16 Example: Using The Standard Normal Table Find the cumulative area that corresponds to a z-score of 1.15. Solution: Find 1.1 in the left hand column. Move across the row to the column under 0.05 The area to the left of z = 1.15 is 0.8749. 17 Example: Using The Standard Normal Table Find the cumulative area that corresponds to a z-score of -0.24. Solution: Find -0.2 in the left hand column. Move across the row to the column under 0.04 The area to the left of z = -0.24 is 0.4052. 18 Finding Areas Under the Standard Normal Curve 1. Sketch the standard normal curve and shade the appropriate area under the curve. 2. Find the area by following the directions for each case shown. a. To find the area to the left of z, find the area that corresponds to z in the Standard Normal Table. 2. The area to the left of z = 1.23 is 0.8907 1. Use the table to find the area for the z-score 19 Finding Areas Under the Standard Normal Curve b. To find the area to the right of z, use the Standard Normal Table to find the area that corresponds to z. Then subtract the area from 1. 2. The area to the left of z = 1.23 is 0.8907. 3. Subtract to find the area to the right of z = 1.23: 1 0.8907 = 0.1093. 1. Use the table to find the area for the z-score. 20 Finding Areas Under the Standard Normal Curve c. To find the area between two z-scores, find the area corresponding to each z-score in the Standard Normal Table. Then subtract the smaller area from the larger area. 2. The area to the left of z = 1.23 is 0.8907. 3. The area to the left of z = 0.75 is 0.2266. 4. Subtract to find the area of the region between the two z-scores: 0.8907 0.2266 = 0.6641. 1. Use the table to find the area for the z-scores. 21 Example: Finding Area Under the Standard Normal Curve Find the area under the standard normal curve to the left of z = -0.99. Solution: 0.1611 0.99 z 0 From the Standard Normal Table, the area is equal to 0.1611. 22 Example: Finding Area Under the Standard Normal Curve Find the area under the standard normal curve to the right of z = 1.06. Solution: 1 0.8554 = 0.1446 0.8554 z 0 1.06 From the Standard Normal Table, the area is equal to 0.1446. 23 Example: Finding Area Under the Standard Normal Curve Find the area under the standard normal curve between z = 1.5 and z = 1.25. Solution: 0.8944 0.0668 = 0.8276 0.8944 0.0668 1.50 0 1.25 z From the Standard Normal Table, the area is equal to 0.8276. 24 Section 5.1 Summary • Interpreted graphs of normal probability distributions • Found areas under the standard normal curve 25 Test1 data before and after the curve: Section 5.2 Normal Distributions: Finding Probabilities 27 Section 5.2 Objectives • Find probabilities for normally distributed variables 28 Probability and Normal Distributions • If a random variable x is normally distributed, you can find the probability that x will fall in a given interval by calculating the area under the normal curve for that interval. μ = 500 σ = 100 P(x < 600) = Area x μ =500 600 29 Probability and Normal Distributions Normal Distribution Standard Normal Distribution μ = 500 σ = 100 μ=0 σ=1 P(x < 600) x 600 500 z 1 100 P(z < 1) z x μ =500 600 μ=0 1 Same Area P(x < 600) = P(z < 1) 30 Example: Finding Probabilities for Normal Distributions A survey indicates that people use their computers an average of 2.4 years before upgrading to a new machine. The standard deviation is 0.5 year. A computer owner is selected at random. Find the probability that he or she will use it for fewer than 2 years before upgrading. Assume that the variable x is normally distributed. 31 Solution: Finding Probabilities for Normal Distributions Normal Distribution μ = 2.4 σ = 0.5 Standard Normal Distribution μ=0 σ=1 x 2 2.4 z 0.80 0.5 P(x < 2) P(z < -0.80) 0.2119 z x 2 2.4 -0.80 0 P(x < 2) = P(z < -0.80) = 0.2119 32 Example: Finding Probabilities for Normal Distributions A survey indicates that for each trip to the supermarket, a shopper spends an average of 45 minutes with a standard deviation of 12 minutes in the store. The length of time spent in the store is normally distributed and is represented by the variable x. A shopper enters the store. Find the probability that the shopper will be in the store for between 24 and 54 minutes. 33 Solution: Finding Probabilities for Normal Distributions Normal Distribution μ = 45 σ = 12 x- Standard Normal Distribution μ=0 σ=1 24 - 45 -1.75 12 x - 54 - 45 z2 0.75 12 z1 P(24 < x < 54) P(-1.75 < z < 0.75) 0.7734 0.0401 x 24 45 54 z -1.75 0 0.75 P(24 < x < 54) = P(-1.75 < z < 0.75) = 0.7734 – 0.0401 = 0.7333 34 Example: Finding Probabilities for Normal Distributions Find the probability that the shopper will be in the store more than 39 minutes. (Recall μ = 45 minutes and σ = 12 minutes) 35 Solution: Finding Probabilities for Normal Distributions Normal Distribution μ = 45 σ = 12 z P(x > 39) Standard Normal Distribution μ=0 σ=1 x- 39 - 45 -0.50 12 P(z > -0.50) 0.3085 z x 39 45 -0.50 0 P(x > 39) = P(z > -0.50) = 1– 0.3085 = 0.6915 36 Example: Finding Probabilities for Normal Distributions If 200 shoppers enter the store, how many shoppers would you expect to be in the store more than 39 minutes? Solution: Recall P(x > 39) = 0.6915 200(0.6915) =138.3 (or about 138) shoppers 37 Example: Using Technology to find Normal Probabilities Assume that cholesterol levels of men in the United States are normally distributed, with a mean of 215 milligrams per deciliter and a standard deviation of 25 milligrams per deciliter. You randomly select a man from the United States. What is the probability that his cholesterol level is less than 175? Use a technology tool to find the probability. 38 In fact, modern humans are the only adult mammals, excluding some domesticated animals, with a mean LDL level over 80 mg/dl and a total cholesterol over 160 mg/dl 15 and 16 (Fig. 1). Thus, although an LDL level of 50 to 70 mg/dl seems excessively low by modern American standards, it is precisely the normal range for individuals living the lifestyle and eating the diet for which we are genetically adapted. Solution: Using Technology to find Normal Probabilities Must specify the mean, standard deviation, and the xvalue(s) that determine the interval. 40 Section 5.2 Summary • Found probabilities for normally distributed variables 41 Section 5.3 Normal Distributions: Finding Values 42 Section 5.3 Objectives • Find a z-score given the area under the normal curve • Transform a z-score to an x-value • Find a specific data value of a normal distribution given the probability 43 Finding values Given a Probability • In section 5.2 we were given a normally distributed random variable x and we were asked to find a probability. • In this section, we will be given a probability and we will be asked to find the value of the random variable x. 5.2 x z probability 5.3 44 Example: Finding a z-Score Given an Area Find the z-score that corresponds to a cumulative area of 0.3632. Solution: 0.3632 z z 0 45 Solution: Finding a z-Score Given an Area • Locate 0.3632 in the body of the Standard Normal Table. The z-score is -0.35. • The values at the beginning of the corresponding row and at the top of the column give the z-score. 46 Example: Finding a z-Score Given an Area Find the z-score that has 10.75% of the distribution’s area to its right. Solution: 1 – 0.1075 = 0.8925 0.1075 z 0 z Because the area to the right is 0.1075, the cumulative area is 0.8925. 47 Solution: Finding a z-Score Given an Area • Locate 0.8925 in the body of the Standard Normal Table. The z-score is 1.24. • The values at the beginning of the corresponding row and at the top of the column give the z-score. 48 Example: Finding a z-Score Given a Percentile Find the z-score that corresponds to P5. Solution: The z-score that corresponds to P5 is the same z-score that corresponds to an area of 0.05. 0.05 z 0 z The areas closest to 0.05 in the table are 0.0495 (z = -1.65) and 0.0505 (z = -1.64). Because 0.05 is halfway between the two areas in the table, use the z-score that is halfway between -1.64 and -1.65. The z-score is -1.645. 49 Transforming a z-Score to an x-Score To transform a standard z-score to a data value x in a given population, use the formula x = μ + zσ 50 Example: Finding an x-Value The speeds of vehicles along a stretch of highway are normally distributed, with a mean of 67 miles per hour and a standard deviation of 4 miles per hour. Find the speeds x corresponding to z-sores of 1.96, -2.33, and 0. Solution: Use the formula x = μ + zσ • z = 1.96: x = 67 + 1.96(4) = 74.84 miles per hour • z = -2.33: x = 67 + (-2.33)(4) = 57.68 miles per hour • z = 0: x = 67 + 0(4) = 67 miles per hour Notice 74.84 mph is above the mean, 57.68 mph is below the mean, and 67 mph is equal to the mean. 51 Example: Finding a Specific Data Value Scores for a civil service exam are normally distributed, with a mean of 75 and a standard deviation of 6.5. To be eligible for civil service employment, you must score in the top 5%. What is the lowest score you can earn and still be eligible for employment? Solution: 1 – 0.05 = 0.95 0 75 5% ? ? z x An exam score in the top 5% is any score above the 95th percentile. Find the z-score that corresponds to a cumulative area of 0.95. 52 Solution: Finding a Specific Data Value From the Standard Normal Table, the areas closest to 0.95 are 0.9495 (z = 1.64) and 0.9505 (z = 1.65). Because 0.95 is halfway between the two areas in the table, use the z-score that is halfway between 1.64 and 1.65. That is, z = 1.645. 5% 0 75 1.645 ? z x 53 Solution: Finding a Specific Data Value Using the equation x = μ + zσ x = 75 + 1.645(6.5) ≈ 85.69 5% 0 1.645 75 85.69 z x The lowest score you can earn and still be eligible for employment is 86. 54 Section 5.3 Summary • Found a z-score given the area under the normal curve • Transformed a z-score to an x-value • Found a specific data value of a normal distribution given the probability 55 Section 5.4 Sampling Distributions and the Central Limit Theorem 56 Section 5.4 Objectives • Find sampling distributions and verify their properties • Interpret the Central Limit Theorem • Apply the Central Limit Theorem to find the probability of a sample mean 57 Sampling Distributions Sampling distribution • The probability distribution of a sample statistic. • Formed when samples of size n are repeatedly taken from a population. • e.g. Sampling distribution of sample means 58 Properties of Sampling Distributions of Sample Means 1. The mean of the sample means, x , is equal to the population mean μ. x 2. The standard deviation of the sample means, x , is equal to the population standard deviation, σ divided by the square root of the sample size, n. x n • Called the standard error of the mean. 59 Example: Sampling Distribution of Sample Means The population values {1, 3, 5, 7} are written on slips of paper and put in a box. Two slips of paper are randomly selected, with replacement. a. Find the mean, variance, and standard deviation of the population. Solution: Mean: x 4 N 2 ( x ) Variance: 2 5 N Standard Deviation: 5 2.236 61 Example: Sampling Distribution of Sample Means b. Graph the probability histogram for the population values. Solution: Probability Histogram of Population of x P(x) 0.25 Probability All values have the same probability of being selected (uniform distribution) x 1 3 5 7 Population values 62 Example: Sampling Distribution of Sample Means c. List all the possible samples of size n = 2 and calculate the mean of each sample. Solution: Sample 1, 1 1, 3 1, 5 1, 7 3, 1 3, 3 3, 5 3, 7 x 1 2 3 4 2 3 4 5 Sample 5, 1 5, 3 5, 5 5, 7 7, 1 7, 3 7, 5 7, 7 x 3 4 5 6 4 5 6 7 These means form the sampling distribution of sample means. 63 Example: Sampling Distribution of Sample Means d. Construct the probability distribution of the sample means. Solution: f Probability x x f Probability 1 1 0.0625 2 3 4 5 2 3 4 3 0.1250 0.1875 0.2500 0.1875 6 7 2 1 0.1250 0.0625 64 Example: Sampling Distribution of Sample Means e. Find the mean, variance, and standard deviation of the sampling distribution of the sample means. Solution: The mean, variance, and standard deviation of the 16 sample means are: x 4 2 5 x2 2.5 x 2.5 1.581 2 n These results satisfy the properties of sampling distributions of sample means. x 4 x n 5 2.236 1.581 2 2 65 Example: Sampling Distribution of Sample Means f. Graph the probability histogram for the sampling distribution of the sample means. Solution: P(x) Probability 0.25 Probability Histogram of Sampling Distribution of x 0.20 0.15 0.10 0.05 x 2 3 4 5 6 The shape of the graph is symmetric and bell shaped. It approximates a normal distribution. 7 Sample mean 66 The Central Limit Theorem 1. If samples of size n 30, are drawn from any population with mean = and standard deviation = , x then the sampling distribution of the sample means approximates a normal distribution. The greater the sample size, the better the approximation. xx x x x x x x x x x x x 67 The Central Limit Theorem 2. If the population itself is normally distributed, x the sampling distribution of the sample means is normally distribution for any sample size n. xx x x x x x x x x x x x 68 The Central Limit Theorem • In either case, the sampling distribution of sample means has a mean equal to the population mean. x • The sampling distribution of sample means has a variance equal to 1/n times the variance of the population and a standard deviation equal to the population standard deviation divided by the square root of n. 2 x2 x n n Variance Standard deviation (standard error of the mean) 69 The Central Limit Theorem 1. Any Population Distribution Distribution of Sample Means, n ≥ 30 2. Normal Population Distribution Distribution of Sample Means, (any n) 70 Example: Interpreting the Central Limit Theorem Phone bills for residents of a city have a mean of $64 and a standard deviation of $9. Random samples of 36 phone bills are drawn from this population and the mean of each sample is determined. Find the mean and standard error of the mean of the sampling distribution. Then sketch a graph of the sampling distribution of sample means. 71 Solution: Interpreting the Central Limit Theorem • The mean of the sampling distribution is equal to the population mean x 64 • The standard error of the mean is equal to the population standard deviation divided by the square root of n. x n 9 1.5 36 72 Solution: Interpreting the Central Limit Theorem • Since the sample size is greater than 30, the sampling distribution can be approximated by a normal distribution with x 1.5 x 64 73 Example: Interpreting the Central Limit Theorem The heights of fully grown white oak trees are normally distributed, with a mean of 90 feet and standard deviation of 3.5 feet. Random samples of size 4 are drawn from this population, and the mean of each sample is determined. Find the mean and standard error of the mean of the sampling distribution. Then sketch a graph of the sampling distribution of sample means. 74 Solution: Interpreting the Central Limit Theorem • The mean of the sampling distribution is equal to the population mean x 90 • The standard error of the mean is equal to the population standard deviation divided by the square root of n. x n 3.5 1.75 4 75 Solution: Interpreting the Central Limit Theorem • Since the population is normally distributed, the sampling distribution of the sample means is also normally distributed. x 1.75 x 90 76 Probability and the Central Limit Theorem • To transform x to a z-score x x x Value-Mean z Standard Error x n 77 Example: Probabilities for Sampling Distributions The graph shows the length of time people spend driving each day. You randomly select 50 drivers age 15 to 19. What is the probability that the mean time they spend driving each day is between 24.7 and 25.5 minutes? Assume that σ = 1.5 minutes. 79 Solution: Probabilities for Sampling Distributions From the Central Limit Theorem (sample size is greater than 30), the sampling distribution of sample means is approximately normal with x 25 x n 1.5 0.21213 50 80 Solution: Probabilities for Sampling Distributions Normal Distribution Standard Normal Distribution μ = 25 σ = 0.21213 x - 24.7 - 25 μ=0 σ=1 z1 -1.41 1.5 n 50 P(-1.41 < z < 2.36) P(24.7 < x < 25.5) z2 x- n 25.5 - 25 2.36 1.5 50 0.9909 0.0793 x 24.7 25 25.5 z -1.41 0 2.36 P(24 < x < 54) = P(-1.41 < z < 2.36) = 0.9909 – 0.0793 = 0.9116 81 Example: Probabilities for x and x A bank auditor claims that credit card balances are normally distributed, with a mean of $2870 and a standard deviation of $900. 1. What is the probability that a randomly selected credit card holder has a credit card balance less than $2500? Solution: You are asked to find the probability associated with a certain value of the random variable x. 82 Solution: Probabilities for x and x Normal Distribution μ = 2870 σ = 900 P(x < 2500) z Standard Normal Distribution μ=0 σ=1 x- 2500 - 2870 -0.41 900 P(z < -0.41) 0.3409 x 2500 2870 z -0.41 0 P( x < 2500) = P(z < -0.41) = 0.3409 83 Example: Probabilities for x and x 2. You randomly select 25 credit card holders. What is the probability that their mean credit card balance is less than $2500? Solution: You are asked to find the probability associated with a sample mean x. x 2870 x n 900 180 25 84 Solution: Probabilities for x and x Normal Distribution μ = 2870 σ = 180 z Standard Normal Distribution μ=0 σ=1 x- n 2500 - 2870 -2.06 900 25 P(z < -2.06) P(x < 2500) 0.0197 x 2500 2870 z -2.06 0 P( x < 2500) = P(z < -2.06) = 0.0197 85 Solution: Probabilities for x and x • There is a 34% chance that an individual will have a balance less than $2500. • There is only a 2% chance that the mean of a sample of 25 will have a balance less than $2500 (unusual event). • It is possible that the sample is unusual or it is possible that the auditor’s claim that the mean is $2870 is incorrect. 86 Section 5.4 Summary • Found sampling distributions and verify their properties • Interpreted the Central Limit Theorem • Applied the Central Limit Theorem to find the probability of a sample mean 87 Section 5.5 Normal Approximations to Binomial Distributions 88 Section 5.5 Objectives • Determine when the normal distribution can approximate the binomial distribution • Find the correction for continuity • Use the normal distribution to approximate binomial probabilities 89 Normal Approximation to a Binomial • The normal distribution is used to approximate the binomial distribution when it would be impractical to use the binomial distribution to find a probability. Normal Approximation to a Binomial Distribution • If np 5 and nq 5, then the binomial random variable x is approximately normally distributed with mean μ = np standard deviation σ npq 90 Normal Approximation to a Binomial • Binomial distribution: p = 0.25 • As n increases the histogram approaches a normal curve. 91 Example: Approximating the Binomial Decide whether you can use the normal distribution to approximate x, the number of people who reply yes. If you can, find the mean and standard deviation. 1. Fifty-one percent of adults in the U.S. whose New Year’s resolution was to exercise more achieved their resolution. You randomly select 65 adults in the U.S. whose resolution was to exercise more and ask each if he or she achieved that resolution. 92 Solution: Approximating the Binomial • You can use the normal approximation n = 65, p = 0.51, q = 0.49 np = (65)(0.51) = 33.15 ≥ 5 nq = (65)(0.49) = 31.85 ≥ 5 • Mean: μ = np = 33.15 • Standard Deviation: σ npq 65 0.51 0.49 4.03 93 Example: Approximating the Binomial Decide whether you can use the normal distribution to approximate x, the number of people who reply yes. If you can find, find the mean and standard deviation. 2. Fifteen percent of adults in the U.S. do not make New Year’s resolutions. You randomly select 15 adults in the U.S. and ask each if he or she made a New Year’s resolution. 94 Solution: Approximating the Binomial • You cannot use the normal approximation n = 15, p = 0.15, q = 0.85 np = (15)(0.15) = 2.25 < 5 nq = (15)(0.85) = 12.75 ≥ 5 • Because np < 5, you cannot use the normal distribution to approximate the distribution of x. 95 Correction for Continuity • The binomial distribution is discrete and can be represented by a probability histogram. • To calculate exact binomial probabilities, the binomial formula is used for each value of x and the results are added. • Geometrically this corresponds to adding the areas of bars in the probability histogram. 96 Correction for Continuity • When you use a continuous normal distribution to approximate a binomial probability, you need to move 0.5 unit to the left and right of the midpoint to include all possible x-values in the interval (correction for continuity). Exact binomial probability P(x = c) c Normal approximation P(c – 0.5 < x < c + 0.5) c– 0.5 c c+ 0.5 97 Example: Using a Correction for Continuity Use a correction for continuity to convert the binomial intervals to a normal distribution interval. 1. The probability of getting between 270 and 310 successes, inclusive. Solution: • The discrete midpoint values are 270, 271, …, 310. • The corresponding interval for the continuous normal distribution is 269.5 < x < 310.5 98 Example: Using a Correction for Continuity Use a correction for continuity to convert the binomial intervals to a normal distribution interval. 2. The probability of getting at least 158 successes. Solution: • The discrete midpoint values are 158, 159, 160, …. • The corresponding interval for the continuous normal distribution is x > 157.5 99 Example: Using a Correction for Continuity Use a correction for continuity to convert the binomial intervals to a normal distribution interval. 3. The probability of getting less than 63 successes. Solution: • The discrete midpoint values are …,60, 61, 62. • The corresponding interval for the continuous normal distribution is x < 62.5 100 Using the Normal Distribution to Approximate Binomial Probabilities In Words 1. Verify that the binomial distribution applies. 2. Determine if you can use the normal distribution to approximate x, the binomial variable. 3. Find the mean and standard deviation for the distribution. In Symbols Specify n, p, and q. Is np 5? Is nq 5? np npq 101 Using the Normal Distribution to Approximate Binomial Probabilities In Words 4. Apply the appropriate continuity correction. Shade the corresponding area under the normal curve. 5. Find the corresponding zscore(s). 6. Find the probability. In Symbols Add or subtract 0.5 from endpoints. z x- Use the Standard Normal Table. 102 Example: Approximating a Binomial Probability Fifty-one percent of adults in the U. S. whose New Year’s resolution was to exercise more achieved their resolution. You randomly select 65 adults in the U. S. whose resolution was to exercise more and ask each if he or she achieved that resolution. What is the probability that fewer than forty of them respond yes? (Source: Opinion Research Corporation) Solution: • Can use the normal approximation (see slide 89) μ = 65∙0.51 = 33.15 σ 65 0.51 0.49 4.03 103 Solution: Approximating a Binomial Probability • Apply the continuity correction: Fewer than 40 (…37, 38, 39) corresponds to the continuous normal distribution interval x < 39.5 Normal Distribution μ = 33.15 σ = 4.03 P(x < 39.5) z x- Standard Normal μ=0 σ=1 39.5 - 33.15 1.58 4.03 P(z < 1.58) 0.9429 x μ =33.15 39.5 z μ =0 1.58 P(z < 1.58) = 0.9429 104 Example: Approximating a Binomial Probability A survey reports that 86% of Internet users use Windows® Internet Explorer ® as their browser. You randomly select 200 Internet users and ask each whether he or she uses Internet Explorer as his or her browser. What is the probability that exactly 176 will say yes? (Source: 0neStat.com) Solution: • Can use the normal approximation np = (200)(0.86) = 172 ≥ 5 nq = (200)(0.14) = 28 ≥ 5 μ = 200∙0.86 = 172 σ 200 0.86 0.14 4.91 105 Solution: Approximating a Binomial Probability • Apply the continuity correction: Exactly 176 corresponds to the continuous normal distribution interval 175.5 < x < 176.5 Normal Distribution Standard Normal μ = 172 σ = 4.91 x - 175.5 - 172 μ=0 σ=1 z 0.71 1 P(175.5 < x < 176.5) 4.91 x - 176.5 - 172 z2 0.92 4.91 P(0.71 < z < 0.92) 0.8212 0.7611 z x μ =172 176.5 175.5 μ =0 0.92 0.71 P(0.71 < z < 0.92) = 0.8212 – 0.7611 = 0.0601 106 Section 5.5 Summary • Determined when the normal distribution can approximate the binomial distribution • Found the correction for continuity • Used the normal distribution to approximate binomial probabilities 107 Chapter 5: Normal Probability Distributions Elementary Statistics: Picturing the World Fifth Edition by Larson and Farber Slide 4- 108 Find the probability using the standard normal distribution. P(z < 1.49) A. 0.9319 B. 0.0681 C. 0.6879 D. 0.3121 Slide 5- 109 Find the probability using the standard normal distribution. P(z < 1.49) A. 0.9319 B. 0.0681 C. 0.6879 D. 0.3121 Slide 5- 110 Find the probability using the standard normal distribution. P(z ≥ –2.31) A. 0.0104 B. 0.0087 C. 0.9896 D. 0.9913 Slide 5- 111 Find the probability using the standard normal distribution. P(z ≥ –2.31) A. 0.0104 B. 0.0087 C. 0.9896 D. 0.9913 Slide 5- 112 Find the probability using the standard normal distribution. P(–2.14 < z < 0.95) A. 0.1170 B. 0.0681 C. 0.1873 D. 0.8127 Slide 5- 113 Find the probability using the standard normal distribution. P(–2.14 < z < 0.95) A. 0.1170 B. 0.0681 C. 0.1873 D. 0.8127 Slide 5- 114 IQ scores are normally distributed with a mean of 100 and a standard deviation of 15. Find the probability a randomly selected person has an IQ score greater than 120. A. 0.9082 B. 0.0918 C. 0.6293 D. 0.3707 Slide 5- 115 IQ scores are normally distributed with a mean of 100 and a standard deviation of 15. Find the probability a randomly selected person has an IQ score greater than 120. A. 0.9082 B. 0.0918 C. 0.6293 D. 0.3707 IQ scores are normally distributed with a mean of 100 and a standard deviation of 15. Find the probability a randomly selected person has an IQ score between 100 and 120. A. 0.9082 B. 0.0918 C. 0.4082 D. 0.5918 Slide 5- 117 IQ scores are normally distributed with a mean of 100 and a standard deviation of 15. Find the probability a randomly selected person has an IQ score between 100 and 120. A. 0.9082 B. 0.0918 C. 0.4082 D. 0.5918 Slide 5- 118 Find the z-score that has 2.68% of the distribution’s area to its right. A. z = 0.9963 B. z = –1.93 C. z = –0.0037 D. z = 1.93 Slide 5- 119 Find the z-score that has 2.68% of the distribution’s area to its right. A. z = 0.9963 B. z = –1.93 C. z = –0.0037 D. z = 1.93 Slide 5- 120 IQ scores are normally distributed with a mean of 100 and a standard deviation of 15. What IQ score represents the 98th percentile? A. 131 B. 69 C. 113 D. 145 Slide 5- 121 IQ scores are normally distributed with a mean of 100 and a standard deviation of 15. What IQ score represents the 98th percentile? A. 131 B. 69 C. 113 D. 145 Slide 5- 122 A population has a mean of 80 and a standard deviation of 12. Samples of size 36 are selected from the population. Describe the sampling distribution of x . A. Normal, x 80, x 2 B. Normal, x 80, x 12 C. Approximately normal, x 80, x 2 D. Approximately normal, x 80, x 12 Slide 5- 123 A population has a mean of 80 and a standard deviation of 12. Samples of size 36 are selected from the population. Describe the sampling distribution of x . A. Normal, x 80, x 2 B. Normal, x 80, x 12 C. Approximately normal, x 80, x 2 D. Approximately normal, x 80, x 12 Slide 5- 124 American children watch an average of 25 hours of television per week with a standard deviation of 8 hours. A random sample of 40 children is selected. What is the probability the mean number of hours of television they watch per week is less than 22? A. 0.3520 B. 0.0089 C. 0.9911 D. 0.6480 Slide 5- 125 American children watch an average of 25 hours of television per week with a standard deviation of 8 hours. A random sample of 40 children is selected. What is the probability the mean number of hours of television they watch per week is less than 22? A. 0.3520 B. 0.0089 C. 0.9911 D. 0.6480 Slide 5- 126 Use a correction for continuity to convert the following interval to a normal distribution interval. The probability of getting at least 80 successes A. x > 80.5 B. x > 79.5 C. x < 80.5 D. x < 79.5 Slide 5- 127 Use a correction for continuity to convert the following interval to a normal distribution interval. The probability of getting at least 80 successes A. x > 80.5 B. x > 79.5 C. x < 80.5 D. x < 79.5 Slide 5- 128