# Chapter 6 The Normal Distribution Copyright 2016 Pearson

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Chapter 6 The Normal Distribution Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 1

Objectives In this chapter, you learn: n n n To compute probabilities from the normal distribution How to use the normal distribution to solve business problems To use the normal probability plot to determine whether a set of data is approximately normally distributed Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 2

Continuous Probability Distributions n A continuous variable is a variable that can assume any value on a continuum (can assume an uncountable number of values) n n n thickness of an item time required to complete a task temperature of a solution height, in inches These can potentially take on any value depending only on the ability to precisely and accurately measure Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 3

The Normal Distribution Bell Shaped n Symmetrical n Mean, Median and Mode are Equal Location is determined by the mean, μ n Spread is determined by the standard deviation, σ The random variable has an infinite theoretical range: + to Copyright © 2016 Pearson Education, Ltd. f(X) σ X μ Mean = Median = Mode Chapter 6, Slide 4

The Normal Distribution Density Function n The formula for the normal probability density function is Where e = the mathematical constant approximated by 2. 71828 π = the mathematical constant approximated by 3. 14159 μ = the population mean σ = the population standard deviation X = any value of the continuous variable Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 5

By varying the parameters μ and σ, we obtain different normal distributions A B C A and B have the same mean but different standard deviations. B and C have different means and different standard deviations. Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 6

The Normal Distribution Shape f(X) Changing μ shifts the distribution left or right. σ Changing σ increases or decreases the spread. μ Copyright © 2016 Pearson Education, Ltd. X Chapter 6, Slide 7

The Standardized Normal n n n Any normal distribution (with any mean and standard deviation combination) can be transformed into the standardized normal distribution (Z) To compute normal probabilities need to transform X units into Z units The standardized normal distribution (Z) has a mean of 0 and a standard deviation of 1 Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 8

Translation to the Standardized Normal Distribution n Translate from X to the standardized normal (the “Z” distribution) by subtracting the mean of X and dividing by its standard deviation: The Z distribution always has mean = 0 and standard deviation = 1 Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 9

The Standardized Normal Probability Density Function n The formula for the standardized normal probability density function is Where e = the mathematical constant approximated by 2. 71828 π = the mathematical constant approximated by 3. 14159 Z = any value of the standardized normal distribution Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 10

The Standardized Normal Distribution n Also known as the “Z” distribution Mean is 0 Standard Deviation is 1 f(Z) 1 0 Z Values above the mean have positive Z-values. Values below the mean have negative Z-values. Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 11

Example n n If X is distributed normally with mean of $100 and standard deviation of $50, the Z value for X = $200 is This says that X = $200 is two standard deviations (2 increments of $50 units) above the mean of $100. Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 12

Comparing X and Z units $100 0 $200 2. 0 $X (μ = $100, σ = $50) Z (μ = 0, σ = 1) Note that the shape of the distribution is the same, only the scale has changed. We can express the problem in the original units (X in dollars) or in standardized units (Z) Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 13

Finding Normal Probabilities Probability is measured by the area under the curve f(X) P (a ≤ X ≤ b) = P (a < X < b) (Note that the probability of any individual value is zero) a b Copyright © 2016 Pearson Education, Ltd. X Chapter 6, Slide 14

Probability as Area Under the Curve The total area under the curve is 1. 0, and the curve is symmetric, so half is above the mean, half is below f(X) 0. 5 μ Copyright © 2016 Pearson Education, Ltd. X Chapter 6, Slide 15

The Standardized Normal Table n The Cumulative Standardized Normal table in the textbook (Appendix table E. 2) gives the probability less than a desired value of Z (i. e. , from negative infinity to Z) 0. 9772 Example: P(Z < 2. 00) = 0. 9772 0 Copyright © 2016 Pearson Education, Ltd. 2. 00 Z Chapter 6, Slide 16

The Standardized Normal Table (continued) The column gives the value of Z to the second decimal point Z The row shows the value of Z to the first decimal point 0. 00 0. 01 0. 02 … 0. 0 0. 1 . . . 2. 0 . 9772 The value within the table gives the probability from Z = up to the desired Z value 2. 0 P(Z < 2. 00) = 0. 9772 Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 17

General Procedure for Finding Normal Probabilities To find P(a < X < b) when X is distributed normally: n Draw the normal curve for the problem in terms of X n Translate X-values to Z-values n Use the Standardized Normal Table Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 18

Finding Normal Probabilities n n Let X represent the time it takes (in seconds) to download an image file from the internet. Suppose X is normal with a mean of 18. 0 seconds and a standard deviation of 5. 0 seconds. Find P(X < 18. 6) 18. 0 X 18. 6 Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 19

Finding Normal Probabilities n n (continued) Let X represent the time it takes, in seconds to download an image file from the internet. Suppose X is normal with a mean of 18. 0 seconds and a standard deviation of 5. 0 seconds. Find P(X < 18. 6) μ = 18 σ=5 18 18. 6 μ=0 σ=1 X P(X < 18. 6) Copyright © 2016 Pearson Education, Ltd. Z 0 0. 12 P(Z < 0. 12) Chapter 6, Slide 20

Solution: Finding P(Z < 0. 12) Standardized Normal Probability Table (Portion) Z . 00 . 01 P(X < 18. 6) = P(Z < 0. 12) . 02 0. 5478 0. 0. 5000. 5040. 5080 0. 1. 5398. 5438. 5478 0. 2. 5793. 5832. 5871 0. 3. 6179. 6217. 6255 Copyright © 2016 Pearson Education, Ltd. 0. 00 Z 0. 12 Chapter 6, Slide 21

Finding Normal Upper Tail Probabilities n n Suppose X is normal with mean 18. 0 and standard deviation 5. 0. Now Find P(X > 18. 6) 18. 0 X 18. 6 Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 22

Finding Normal Upper Tail Probabilities (continued) n Now Find P(X > 18. 6)… P(X > 18. 6) = P(Z > 0. 12) = 1. 0 - P(Z ≤ 0. 12) = 1. 0 - 0. 5478 = 0. 4522 0. 5478 1. 000 Z 0 0. 12 Copyright © 2016 Pearson Education, Ltd. 1. 0 - 0. 5478 = 0. 4522 Z 0 0. 12 Chapter 6, Slide 23

Finding a Normal Probability Between Two Values n Suppose X is normal with mean 18. 0 and standard deviation 5. 0. Find P(18 < X < 18. 6) Calculate Z-values: 18 18. 6 X 0 0. 12 Z P(18 < X < 18. 6) = P(0 < Z < 0. 12) Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 24

Solution: Finding P(0 < Z < 0. 12) Standardized Normal Probability P(18 < X < 18. 6) = P(0 < Z < 0. 12) Table (Portion) = P(Z < 0. 12) – P(Z ≤ 0) Z. 00. 01. 02 = 0. 5478 - 0. 5000 = 0. 0478 0. 0. 5000. 5040. 5080 0. 0478 0. 5000 0. 1. 5398. 5438. 5478 0. 2. 5793. 5832. 5871 0. 3. 6179. 6217. 6255 0. 00 Z 0. 12 Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 25

Probabilities in the Lower Tail n n Suppose X is normal with mean 18. 0 and standard deviation 5. 0. Now Find P(17. 4 < X < 18) 18. 0 17. 4 Copyright © 2016 Pearson Education, Ltd. X Chapter 6, Slide 26

Probabilities in the Lower Tail (continued) Now Find P(17. 4 < X < 18)… P(17. 4 < X < 18) = P(-0. 12 < Z < 0) 0. 0478 = P(Z < 0) – P(Z ≤ -0. 12) = 0. 5000 - 0. 4522 = 0. 0478 0. 4522 The Normal distribution is symmetric, so this probability is the same as P(0 < Z < 0. 12) Copyright © 2016 Pearson Education, Ltd. 17. 4 18. 0 -0. 12 0 X Z Chapter 6, Slide 27

Empirical Rule What can we say about the distribution of values around the mean? For any normal distribution: f(X) μ ± 1σ encloses about 68. 26% of X’s σ μ-1σ σ μ μ+1σ X 68. 26% Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 28

The Empirical Rule (continued) n μ ± 2σ covers about 95. 44% of X’s n μ ± 3σ covers about 99. 73% of X’s 2σ 3σ 2σ μ x 95. 44% Copyright © 2016 Pearson Education, Ltd. 3σ μ x 99. 73% Chapter 6, Slide 29

Given a Normal Probability Find the X Value n Steps to find the X value for a known probability: 1. Find the Z value for the known probability 2. Convert to X units using the formula: Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 30

Finding the X value for a Known Probability (continued) Example: n n n Let X represent the time it takes (in seconds) to download an image file from the internet. Suppose X is normal with mean 18. 0 and standard deviation 5. 0 Find X such that 20% of download times are less than X. 0. 2000 ? ? Copyright © 2016 Pearson Education, Ltd. 18. 0 0 X Z Chapter 6, Slide 31

Find the Z value for 20% in the Lower Tail 1. Find the Z value for the known probability Standardized Normal Probability n 20% area in the lower tail is consistent with a Table (Portion) Z value of -0. 84 Z …. 03. 04. 05 -0. 9 …. 1762. 1736. 1711 -0. 8 …. 2033. 2005. 1977 -0. 7 0. 2000 …. 2327. 2296. 2266 ? 18. 0 -0. 84 0 Copyright © 2016 Pearson Education, Ltd. X Z Chapter 6, Slide 32

Finding the X value 2. Convert to X units using the formula: So 20% of the values from a distribution with mean 18. 0 and standard deviation 5. 0 are less than 13. 80 Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 33

Chapter Summary In this chapter we discussed: n n n Computing probabilities from the normal distribution Using the normal distribution to solve business problems Using the normal probability plot to determine whether a set of data is approximately normally distributed Copyright © 2016 Pearson Education, Ltd. Chapter 6, Slide 34