# Chapter 6. Normal Distributions

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1 Chapter 6 Normal Distributions Understandable Statistics Ninth Edition By Brase and Brase Prepared by Yixun Shi Bloomsburg University of Pennsylvania Edited by José Neville Díaz Caraballo University of Puerto Rico at Aguadilla

2 The Normal Distribution A continuous distribution used for modeling many natural phenomena. Sometimes called the Gaussian Distribution, after Carl Gauss. The defining features of a Normal Distribution are the mean, µ, and the standard deviation, σ. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 2

4 Features of the Normal Curve Smooth line and symmetric around µ. Highest point directly above µ. The curve never touches the horizontal axis in either direction. As σ increases, the curve spreads out. As σ decreases, the curve becomes more peaked around µ. Inflection points at µ ± σ. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 4

5 Two Normal Curves Both curves have the same mean, µ = 6. Curve A has a standard deviation of σ = 1. Curve B has a standard deviation of σ = 3. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 5

6 Normal Probability The area under any normal curve will always be 1. The portion of the area under the curve within a given interval represents the probability that a measurement will lie in that interval. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 6

9 Control Charts A graph to examine data over equally spaced time intervals. Used to determine if a variable is in statistical control. Statistical Control: A variable x is in statistical control if it can be described by the same probability distribution over time. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 9

12 Determining if a Variable is Out of Control 1) One point falls beyond the 3σ level. 2) A run of nine consecutive points on one side of the center line. 3) At least two of three consecutive points lie beyond the 2σ level on the same side of the center line. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 12

18 The Standard Normal Distribution Z scores also have a normal distribution µ = 0 σ = 1 Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 18

19 Using the Standard Normal Distribution There are extensive tables for the Standard Normal Distribution. We can determine probabilities for normal distributions: 1) Transform the measurement to a z Score. 2) Utilize Table 5 of Appendix II. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 19

20 Using the Standard Normal Table Table 5(a) gives the cumulative area for a given z value. When calculating a z Score, round to 2 decimal places. For a z Score less than -3.49, use to approximate the area. For a z Score greater than 3.49, use to approximate the area. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 20

24 Example Some doctors believe that a person can lose 5 pounds, on the average, in a month by reducing his/her fat intake and by exercising consistently. Suppose weight loss has a normal distribution. Let = the amount of weight lost (in pounds) by a person in a month. Use a standard deviation of 2 pounds X ~N(5,2). Fill in the blanks. Problem 1 Suppose a person lost 10 pounds in a month. The z-score when pounds is (verify). This z-score tells you that is standard deviations to the (right or left) of the mean (What is the mean?). Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 24

25 Problem 2 Suppose a person gained 3 pounds (a negative weight loss). Then =. This z-score tells you that is standard deviations to the (right or left) of the mean. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 25

26 Suppose the random variables and have the following normal distributions: X~N(5,6) and Y~N(2,1). If X=17 and Y=4, then what is z? Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 26

27 The z-score allows us to compare data that are scaled differently. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 27

28 Normal Probability Final Remarks The probability that z equals a certain number is always 0. P(z = a) = 0 Therefore, < and can be used interchangeably. Similarly, > and can be used interchangeably. P(z < b) = P(z b) P(z > c) = P(z c) Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 28

29 Example 1 An average light bulb manufactured by the Acme Corporation lasts 300 days with a standard deviation of 50 days. Assuming that bulb life is normally distributed, what is the probability that an Acme light bulb will last at most 365 days? Solution Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 29

30 Example 2 Suppose scores on an IQ test are normally distributed. If the test has a mean of 100 and a standard deviation of 10, what is the probability that a person who takes the test will score between 90 and 110? Solution: Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 30

31 Inverse Normal Distribution Sometimes we need to find an x or z that corresponds to a given area under the normal curve. In Table 5, we look up an area and find the corresponding z. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 31

33 The average life of a certain type of motor is 10 years, with a standard deviation of 2 years. If the manufacturer is willing to replace only 3% of the motors that fail, how long a guarantee should he offer? Assume that the lives of the motors follow a normal distribution. Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 33

34 Critical Thinking How to tell if data follow a normal distribution? Histogram a normal distribution s histogram should be roughly bell-shaped. Outliers a normal distribution should have no more than one outlier Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 34

35 Critical Thinking How to tell if data follow a normal distribution? Skewness normal distributions are symmetric. Use the Pearson s index: Pearson s index = 3(x median) s A Pearson s index greater than 1 or less than -1 indicates skewness. Normal quantile plot using a statistical software (see the Using Technology feature.) Copyright Houghton Mifflin Harcourt Publishing Company. All rights reserved. 6 35

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