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Contemporary Mathematics

8.7 Applications of the Normal Distribution

Contemporary Mathematics8.7 Applications of the Normal Distribution

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Table of contents
  1. Preface
  2. 1 Sets
    1. Introduction
    2. 1.1 Basic Set Concepts
    3. 1.2 Subsets
    4. 1.3 Understanding Venn Diagrams
    5. 1.4 Set Operations with Two Sets
    6. 1.5 Set Operations with Three Sets
    7. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  3. 2 Logic
    1. Introduction
    2. 2.1 Statements and Quantifiers
    3. 2.2 Compound Statements
    4. 2.3 Constructing Truth Tables
    5. 2.4 Truth Tables for the Conditional and Biconditional
    6. 2.5 Equivalent Statements
    7. 2.6 De Morgan’s Laws
    8. 2.7 Logical Arguments
    9. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Projects
      5. Chapter Review
      6. Chapter Test
  4. 3 Real Number Systems and Number Theory
    1. Introduction
    2. 3.1 Prime and Composite Numbers
    3. 3.2 The Integers
    4. 3.3 Order of Operations
    5. 3.4 Rational Numbers
    6. 3.5 Irrational Numbers
    7. 3.6 Real Numbers
    8. 3.7 Clock Arithmetic
    9. 3.8 Exponents
    10. 3.9 Scientific Notation
    11. 3.10 Arithmetic Sequences
    12. 3.11 Geometric Sequences
    13. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  5. 4 Number Representation and Calculation
    1. Introduction
    2. 4.1 Hindu-Arabic Positional System
    3. 4.2 Early Numeration Systems
    4. 4.3 Converting with Base Systems
    5. 4.4 Addition and Subtraction in Base Systems
    6. 4.5 Multiplication and Division in Base Systems
    7. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Projects
      5. Chapter Review
      6. Chapter Test
  6. 5 Algebra
    1. Introduction
    2. 5.1 Algebraic Expressions
    3. 5.2 Linear Equations in One Variable with Applications
    4. 5.3 Linear Inequalities in One Variable with Applications
    5. 5.4 Ratios and Proportions
    6. 5.5 Graphing Linear Equations and Inequalities
    7. 5.6 Quadratic Equations with Two Variables with Applications
    8. 5.7 Functions
    9. 5.8 Graphing Functions
    10. 5.9 Systems of Linear Equations in Two Variables
    11. 5.10 Systems of Linear Inequalities in Two Variables
    12. 5.11 Linear Programming
    13. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  7. 6 Money Management
    1. Introduction
    2. 6.1 Understanding Percent
    3. 6.2 Discounts, Markups, and Sales Tax
    4. 6.3 Simple Interest
    5. 6.4 Compound Interest
    6. 6.5 Making a Personal Budget
    7. 6.6 Methods of Savings
    8. 6.7 Investments
    9. 6.8 The Basics of Loans
    10. 6.9 Understanding Student Loans
    11. 6.10 Credit Cards
    12. 6.11 Buying or Leasing a Car
    13. 6.12 Renting and Homeownership
    14. 6.13 Income Tax
    15. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  8. 7 Probability
    1. Introduction
    2. 7.1 The Multiplication Rule for Counting
    3. 7.2 Permutations
    4. 7.3 Combinations
    5. 7.4 Tree Diagrams, Tables, and Outcomes
    6. 7.5 Basic Concepts of Probability
    7. 7.6 Probability with Permutations and Combinations
    8. 7.7 What Are the Odds?
    9. 7.8 The Addition Rule for Probability
    10. 7.9 Conditional Probability and the Multiplication Rule
    11. 7.10 The Binomial Distribution
    12. 7.11 Expected Value
    13. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Formula Review
      4. Projects
      5. Chapter Review
      6. Chapter Test
  9. 8 Statistics
    1. Introduction
    2. 8.1 Gathering and Organizing Data
    3. 8.2 Visualizing Data
    4. 8.3 Mean, Median and Mode
    5. 8.4 Range and Standard Deviation
    6. 8.5 Percentiles
    7. 8.6 The Normal Distribution
    8. 8.7 Applications of the Normal Distribution
    9. 8.8 Scatter Plots, Correlation, and Regression Lines
    10. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  10. 9 Metric Measurement
    1. Introduction
    2. 9.1 The Metric System
    3. 9.2 Measuring Area
    4. 9.3 Measuring Volume
    5. 9.4 Measuring Weight
    6. 9.5 Measuring Temperature
    7. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  11. 10 Geometry
    1. Introduction
    2. 10.1 Points, Lines, and Planes
    3. 10.2 Angles
    4. 10.3 Triangles
    5. 10.4 Polygons, Perimeter, and Circumference
    6. 10.5 Tessellations
    7. 10.6 Area
    8. 10.7 Volume and Surface Area
    9. 10.8 Right Triangle Trigonometry
    10. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  12. 11 Voting and Apportionment
    1. Introduction
    2. 11.1 Voting Methods
    3. 11.2 Fairness in Voting Methods
    4. 11.3 Standard Divisors, Standard Quotas, and the Apportionment Problem
    5. 11.4 Apportionment Methods
    6. 11.5 Fairness in Apportionment Methods
    7. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  13. 12 Graph Theory
    1. Introduction
    2. 12.1 Graph Basics
    3. 12.2 Graph Structures
    4. 12.3 Comparing Graphs
    5. 12.4 Navigating Graphs
    6. 12.5 Euler Circuits
    7. 12.6 Euler Trails
    8. 12.7 Hamilton Cycles
    9. 12.8 Hamilton Paths
    10. 12.9 Traveling Salesperson Problem
    11. 12.10 Trees
    12. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Videos
      4. Formula Review
      5. Projects
      6. Chapter Review
      7. Chapter Test
  14. 13 Math and...
    1. Introduction
    2. 13.1 Math and Art
    3. 13.2 Math and the Environment
    4. 13.3 Math and Medicine
    5. 13.4 Math and Music
    6. 13.5 Math and Sports
    7. Chapter Summary
      1. Key Terms
      2. Key Concepts
      3. Formula Review
      4. Projects
      5. Chapter Review
      6. Chapter Test
  15. A | Co-Req Appendix: Integer Powers of 10
  16. Answer Key
    1. Chapter 1
    2. Chapter 2
    3. Chapter 3
    4. Chapter 4
    5. Chapter 5
    6. Chapter 6
    7. Chapter 7
    8. Chapter 8
    9. Chapter 9
    10. Chapter 10
    11. Chapter 11
    12. Chapter 12
    13. Chapter 13
  17. Index
A hand is holding a pencil and filling in the scantron sheet.
Figure 8.67 Standardized test results generally adhere to the normal distribution. (credit: “Taking a Test” by Marco Verch Professional Photographer/Flickr, CC BY 2.0)

Learning Objectives

After completing this section, you should be able to:

  1. Apply the normal distribution to real-world scenarios.

As we saw in The Normal Distribution, the word “standardized” is closely associated with the normal distribution. This is why tests like college entrance exams, state achievement tests for K–12 students, and Advanced Placement tests are often called “standardized tests”: scores are assigned in a way that forces them to follow a normal distribution, with a mean and standard deviation that are consistent from year to year. Standardization also allows people like college admissions officers to directly compare an applicant who took the ACT (a college entrance exam) to an applicant who instead chose to take the SAT (a different college entrance exam). Standardization allows us to compare individuals from different groups; this is among the most important applications of the normal distribution. We’ll explore this and other real-world uses of the normal distribution in this section.

College Entrance Exams

There are two good ways to compare two data values from different groups: using zz-scores and using percentiles. The two methods will always give consistent results (meaning that we won’t find, for example, that the first value is better using zz-scores but the second value is better using percentiles), so use whichever method is more comfortable for you.

Example 8.41

Evaluating College Entrance Exam Scores

According to the Digest of Education Statistics, composite scores on the SAT have mean 1060 and standard deviation 195, while composite scores on the ACT have mean 21 and standard deviation 5.

  1. At what percentile would an SAT score of 990 fall?
  2. What is the z-score of an ACT score of 27?
  3. Which is better: a score of 1450 on the SAT or 29 on the ACT?

Your Turn 8.41

According to the Graduate Management Admission Council (GMAC), the mean score on the GMAT (an entrance exam for graduate schools in business management) is 565, with standard deviation 116. For the LSAT (an entrance exam for law schools), Kaplan informs us that the mean score is 150 with standard deviation 10.

1.
What is the z-score for a GMAT score of 715?
2.
At what percentile is an LSAT score of 166?
3.
Which is better: a GMAT score of 650 or an LSAT score of 161?

Coin flipping

In the opening of The Normal Distribution, we saw that the number of heads we get when we flip a coin 100 times is distributed normally. It can be shown that if nn is the number of flips, then the mean of that distribution is n2n2 and the standard deviation is n2n2 (as long as n20n20). So, for 100 flips, the mean of the distribution is 50 and the standard deviation is 5. In that opening example, one of our early runs gave us 70 heads in 100 flips, which we noted seemed unusual. Using the normal distribution, we can identify exactly how unusual that really is. Using Google Sheets, the formula “=NORM.DIST(70, 50, 5, TRUE)” gives us 0.999968, which is the 99.997th percentile! How is that useful? Suppose you need to test whether a coin is fair, and so you flip it 100 times. While we might be suspicious if we get 70 heads out of the 100 flips, we now have a numerical measure for how unusual that is: If the coin were fair, we would expect to see 70 heads (or more) only 100-99.9968=0.0032%100-99.9968=0.0032% of the time. That’s really unlikely! Analysis like this is related to hypothesis testing, an important application of statistics in the sciences and social sciences.

Example 8.42

Flipping a Coin

Let’s say we flip a coin 64 times and count the number of heads.

  1. What would be the mean of the corresponding distribution?
  2. What would be the standard deviation of the corresponding distribution?
  3. Suppose we got 25 heads, which seems a little low. At what percentile would 25 heads fall?

Your Turn 8.42

You flip a coin 144 times and count the number of heads.
1.
What is the mean of the corresponding distribution?
2.
What is the standard deviation of the corresponding distribution?
3.
You flipped 81 heads. At what percentile does that fall?

Analyzing Data That Are Normally Distributed

Whenever we’re working with a dataset that has a distribution that looks symmetric and bell-shaped, we can use techniques associated with the normal distribution to analyze the data.

Example 8.43

Using Normal Techniques to Analyze Data

The data in “AvgSAT” contains the average SAT score for students attending every institution of higher learning in the United States for which data is available. In Example 8.12, we created a histogram for these data:

A histogram titled, average SAT scores at US institutions. The horizontal axis representing average SAT ranges from 750 to 1600, in increments of 50. The vertical axis representing frequency ranges from 0 to 300, in increments of 100. The histogram infers the following data. 750 to 800: 4. 800 to 850: 5. 850 to 900: 15. 900 to 950: 25. 950 to 1000: 85. 1000 to 1050: 170. 1050 to 1100: 230. 1100 to 1150: 275. 1150 to 1200: 230. 1200 to 1250: 110. 1250 to 1300: 70. 1300 to 1350: 50. 1350 to 1400: 40. 1400 to 1450: 40. 1450 to 1500: 20. 1500 to 1550: 25. 1550 to 1600: 5. Note: all values are approximate.
Figure 8.68 (data source: https://data.ed.gov)

This distribution is fairly symmetric (it’s just a little right-skewed) and bell-shaped, so we can use normal distribution techniques to analyze the data.

  1. What is the mean of these average SAT scores?
  2. What is the standard deviation of these SAT scores?
  3. Using the answers to the previous two questions, use NORM.DIST in Google Sheets to estimate at what percentile the University at Buffalo in New York (average SAT: 1250) falls.
  4. Use PERCENTRANK to find the actual percentile of the University at Buffalo, and see how close the estimate in the previous question came.

Your Turn 8.43

1.
Again using the data in “AvgSAT”, find the average SAT score of a school at the 35th percentile in two ways: using NORM.INV and using PERCENTILE.

Who Knew?

Political Meddling Exposed

The normal distribution pops up in some unusual places. Recently, a team at Duke University has been using statistics to help identify partisan gerrymandering, where electoral districts have been carefully drawn in a way that benefits one political party over another. In their analysis, they found that hypothetical election results in randomly drawn districts are normally distributed. By using techniques similar to the ones we used above, they can quantify precisely how biased a particular electoral map is by finding the percentile rank of the actual election result on the normal distribution of the hypothetical results. You can find out more about their work at the "Quantifying Gerrymandering" site here.

Check Your Understanding

For the following problems, recall that the SAT exam has mean 1060 and standard deviation 195, and that composite scores on the ACT have mean 21 and standard deviation 5.
49.
At what percentile would an SAT score of 940 fall? Round to the nearest whole number.
50.
What score would be at the 67th percentile on the ACT?
51.
Which is a better score: 1300 on the SAT or 27 on the ACT?
For the following problems, recall that if we flip a coin at least 20 times, the distribution of the number of heads is approximately normal with mean equal to half the number of flips and standard deviation equal to half of the square root of the number of flips.
52.
Suppose we flip a coin 120 times. What are the mean and standard deviation of the corresponding distribution of heads?
53.
Let’s say you flip 70 heads in 120 flips. At what percentile would that fall?
54.
How many heads would be at the 30th percentile? Round to the nearest whole number.
For the following problems, use the data in “World Tax,” which gives the tax revenue of many countries of the world in 2017, expressed as a percentage of their gross domestic products. You can assume that the distribution is approximately normal (but you can make a histogram to check, if you want).
55.
What tax revenue percentage falls at the third quartile? Answer this question using Google Sheets in two ways: using PERCENTILE and using NORM.INV.
56.
What tax revenue percentage falls at the 20th percentile? Answer this question using Google Sheets in two ways: using PERCENTILE and using NORM.INV.
57.
At what percentile does the United Kingdom (25.62%, found on row 46 in the spreadsheet) fall? Answer this question using Google Sheets in two ways: using PERCENTRANK and using NORM.DIST.
58.
At what percentile does Kiribati (21.97%, found on row 62 in the spreadsheet) fall? Answer this question using Google Sheets in two ways: using PERCENTRANK and using NORM.DIST.

Section 8.7 Exercises

For the following exercises, assume we’re looking at results from two different standardized tests. The first, called the ABC, has mean 250 and standard deviation 50. The second, called the XYZ, has mean 80 and standard deviation 10.
1.
What would be the z-score of a result of 322 on the ABC?
2.
What would be the z-score of a result of 57 on the XYZ?
3.
At what percentile would a result of 211 on the ABC fall? Round your answer to the nearest percentile.
4.
At what percentile would a result of 94 on the XYZ fall? Round your answer to the nearest percentile.
5.
What score would fall at the first quartile on the ABC? Round your answer to the nearest whole number.
6.
What score would fall at the 60th percentile on the XYZ? Round your answer to the nearest whole number.
7.
Which score is better: 202 on the ABC or 72 on the XYZ?
8.
Which score is better: 324 on the ABC or 94 on the XYZ?
For the following exercises, recall that if we flip a coin at least 20 times, the distribution of the number of heads is approximately normal with mean equal to half the number of flips and standard deviation equal to half of the square root of the number of flips.
9.
What would be the mean and standard deviation for the number of heads in 80 coin flips?
10.
What would be the mean and standard deviation for the number of heads in 144 coin flips?
11.
How many heads would be at the 80th percentile for 80 coin flips? Round your answer to the nearest whole number.
12.
How many heads would be at the 20th percentile for 144 coin flips? Round your answer to the nearest whole number.
13.
What is the z-score for 51 heads in 80 coin flips? Round your answer to the nearest hundredth.
14.
What is the z-score for 87 heads in 144 coin flips? Round your answer to the nearest hundredth.
15.
Which would be more unusual: flipping 51 heads in 80 tries or flipping 87 heads in 144 tries? How do you know?
For the following exercises, use the data in “Wheat,” which gives the yield of wheat (in bushels) produced over several equally sized plots. (These data were collected as part of an experiment reported in “The experimental error of field trials” by W.B. Mercer and A.D. Hall).
16.
Create a histogram of Yield to check that it’s approximately normally distributed.
17.
Find the mean and standard deviation of Yield. Round answers to the nearest thousandth.
18.
What proportion of yields does the normal distribution predict should fall below 4.56? Round to the nearest thousandth.
19.
At what percentile does the normal distribution predict a yield of 3.6 would fall? Round to the nearest whole number.
20.
What yield does the normal distribution estimate would fall at the 80th percentile? Round to the nearest hundredth.
21.
What yield does the normal distribution estimate would fall at the 95th percentile? Round to the nearest hundredth.
22.
If one of these plots yields 2.8 bushels, would that be a good or bad result? How do you know?
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