1. Normal Probability Distributions
Chapter 1
Normal Probability Distributions
Larson/Farber 4th ed

2. Chapter Outline
1.1 Introduction to Normal Distributions and the Standard Normal Distribution
1.2 Normal Distributions: Finding Probabilities
1.3 Normal Distributions: Finding Values
1.4 Sampling Distributions and the Central Limit Theorem
1.5 Normal Approximations to Binomial Distributions
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3. Introduction to Normal Distributions
Section 1.1
Introduction to Normal Distributions
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4. Section 5.1 Objectives
Interpret graphs of normal probability distributions
Find areas under the standard normal curve
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5. 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.
Continuous probability distribution
The probability distribution of a continuous random variable.
Hours spent studying in a day 6 3 9 15 12 18 24 21
The time spent studying can be any number between 0 and 24.
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6. Properties of Normal Distributions
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
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7. Properties of Normal Distributions
The mean, median, and mode are equal.
The normal curve is bell-shaped and symmetric about the mean.
The total area under the curve is equal to one.
The normal curve approaches, but never touches the x-axis as it extends farther and farther away from the mean. x
Total area = 1 μ
Larson/Farber 4th ed

8. Properties of Normal Distributions
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.
μ  3σ
μ + σ
μ  2σ
μ  σ μ
μ + 2σ
μ + 3σ x
Inflection points
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9. 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
σ = 0.7
μ = 1.5
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10. Same Standard Deviations, Different Means
the curve on the right has a larger mean than the curve on the left
the amount of the shift is equal to the difference in the means

11. Same Means, Different Standard Deviations
the lower curve has a larger standard deviation
the spread of the curve increases with the standard deviation

12. Example: Understanding Mean and Standard Deviation
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.)
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13. Example: Understanding Mean and Standard Deviation
Which curve has the greater standard deviation?
Solution:
Curve B has the greater standard deviation (Curve B is more spread out than curve A.)
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14. 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:
σ = 3.5 (The inflection points are one standard deviation away from the mean)
μ = 90 (A normal curve is symmetric about the mean)
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15. The Standard Normal Distribution
A normal distribution with a mean of 0 and a standard deviation of 1. 3 1 2 1 2 3 z
Area = 1
Any x-value can be transformed into a z-score by using the formula
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16. 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.
m=0
s=1 z
Standard Normal Distribution
Normal Distribution x m s
Use the Standard Normal Table to find the cumulative area under the standard normal curve.
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17. Properties of the Standard Normal Distribution
The cumulative area is close to 0 for z-scores close to z = 3.49.
The cumulative area increases as the z-scores increase. z 3 1 2 1 2 3
z = 3.49
Area is close to 0
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18. Properties of the Standard Normal Distribution
The cumulative area for z = 0 is
The cumulative area is close to 1 for z-scores close to z = 3.49. z 3 1 2 1 2 3
Area is
z = 0
z = 3.49
Area is close to 1
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19. Larson/Farber 4th ed

20. 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
Larson/Farber 4th ed

21. Finding Areas Under the Standard Normal Curve
Sketch the standard normal curve and shade the appropriate area under the curve.
Find the area by following the directions for each case shown.
To find the area to the left of z, find the area that corresponds to z in the Standard Normal Table.
The area to the left of z = 1.23 is
Use the table to find the area for the z-score
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22. Finding Areas Under the Standard Normal Curve
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.
The area to the left of z = 1.23 is
Subtract to find the area to the right of z = 1.23:  =
Use the table to find the area for the z-score.
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23. Finding Areas Under the Standard Normal Curve
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.
The area to the left of z = is
Subtract to find the area of the region between the two z-scores:  =
The area to the left of z = 0.75 is
Use the table to find the area for the z-scores.
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24. Example: Finding Area Under the Standard Normal Curve
Find the area under the standard normal curve to the left of z =
Solution:
0.99 z
0.1611
From the Standard Normal Table, the area is equal to
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25. Example: Finding Area Under the Standard Normal Curve
Find the area under the standard normal curve to the right of z = 1.06.
Solution:
1.06 z
1  =
0.8554
From the Standard Normal Table, the area is equal to
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26. 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 =
1.25 z
1.50
0.8944
0.0668
From the Standard Normal Table, the area is equal to
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27. Section 1.1 Summary
Interpreted graphs of normal probability distributions
Found areas under the standard normal curve
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28. Normal Distributions: Finding Probabilities
Section 1.2
Normal Distributions: Finding Probabilities
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29. Section 1.2 Objectives
Find probabilities for normally distributed variables
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30. 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
600
μ =500 x
P(x < 600) = Area
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31. Probability and Normal Distributions
Standard Normal Distribution
600
μ =500
P(x < 600)
μ = σ = 100 x 1
μ = 0
μ = 0 σ = 1 z
P(z < 1)
Same Area
P(x < 500) = P(z < 1)
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32. 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.
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33. Solution: Finding Probabilities for Normal Distributions
Standard Normal Distribution
-0.80
μ = 0 σ = 1 z
P(z < -0.80) 2
2.4
P(x < 2)
μ = σ = 0.5 x
0.2119
P(x < 2) = P(z < -0.80) =
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34. 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.
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35. Solution: Finding Probabilities for Normal Distributions
-1.75 z
Standard Normal Distribution μ = 0 σ = 1
P(-1.75 < z < 0.75)
0.75 24 45
P(24 < x < 54) x
0.7734
0.0401 54
P(24 < x < 54) = P(-1.75 < z < 0.75)
= – =
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36. 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)
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37. Solution: Finding Probabilities for Normal Distributions
Standard Normal Distribution μ = 0 σ = 1
P(z > -0.50) z
-0.50 39 45
P(x > 39) x
0.3085
P(x > 39) = P(z > -0.50) = 1– =
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38. 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) =
200(0.6915) =138.3 (or about 138) shoppers
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39. 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.
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40. Section 1.2 Summary
Found probabilities for normally distributed variables
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41. Normal Distributions: Finding Values
Section 1.3
Normal Distributions: Finding Values
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42. Section 1.3 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
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43. Finding values Given a Probability
In section 1.2 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
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44. Solution: Finding a z-Score Given an Area
Locate 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.
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45. 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. z
0.05
The areas closest to 0.05 in the table are (z = -1.65) and (z = -1.64). Because 0.05 is halfway between the two areas in the table, use the z-score that is halfway between and The z-score is
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46. 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
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47. 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 = (4) = miles per hour
z = -2.33: x = 67 + (-2.33)(4) = miles per hour
z = 0: x = (4) = 67 miles per hour
Notice mph is above the mean, mph is below the mean, and 67 mph is equal to the mean.
Larson/Farber 4th ed

48. 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: ? z 5% 75 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.
1 – 0.05
= 0.95
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49. Solution: Finding a Specific Data Value
From the Standard Normal Table, the areas closest to 0.95 are (z = 1.64) and (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 That is, z = 5% z
1.645 75 x ?
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50. Solution: Finding a Specific Data Value
Using the equation x = μ + zσ x = (6.5) ≈ 85.69 5% z
1.645 75 x
85.69
The lowest score you can earn and still be eligible for employment is 86.
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51. Section 1.3 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
Larson/Farber 4th ed