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2.2 Normal Distributions
Homework Answers
41.
Men’s Height (inches)
41.
Weight (ounces)
43.
a)
b)
c)
d)
Approximately 2.5%
Between 64 and 74 inches
Approximately 13.5%
84th percentile
44.
a)
b)
c)
d)
Approximately 2.5%
Between 9.07 and 9.17 ounces
Approximately 83.85%
16th percentile
45. The standard deviation is approximately .2 for the taller
distribution, and .5 for the shorter one
46. The mean is 10 and the standard deviation is about 2
47.
a)
b)
c)
d)
.9978
.0022
.9515
.9493
48.
a)
b)
c)
d)
.0069
.0069
.1798
.1004
49.
a)
b)
.8587
.2718
50.
a)
b)
.7621
.241
51.
a)
b)
Z= -1.28
Z= .41
Assessing Normality
 Some distributions are normal distributed (or approximately
normally distributed)
 SAT scores
 Gas mileage
 Chapter 1 test scores
 Other variables are not normally distributed
 Household income
 Military spending
 Survival time after cancer diagnosis
 Etc.
Assessing Normality
 So, before we use techniques that work for normal
distributions, we need to have a way (or ways) to assess
whether a distribution can be considered a normal
distribution
 We will use several methods to assess whether a distribution
is normally distributed
Look at the distribution
2. 68-95-99.7 rule
3. Normal probability plot
1.
Look at the distribution
 Is this normally
distributed?
Look at the distribution
 Is this normally
distributed?
 Probably not. Looks more
like a uniform distribution
Look at the distribution
 When we look at a distribution to see if it is normally
distributed, what are we looking for?
Look at the distribution
 When we look at a distribution to see if it is normally
distributed, what are we looking for?
 Symmetric
 Bell-shaped
 Single-peaked
So what about this one?
 Is it (more or less)
symmetric?
 Is it bell shaped?
 Is it single-peaked?
So what about this one?
 Is it (more or less)
symmetric?
 Debatable, but I would say
yes
 Is it bell shaped?
 NO
 Is it single-peaked?
 NO
And this one?
 Is it (more or less)
symmetric?
 Is it bell shaped?
 Is it single-peaked?
And this one?
 Is it (more or less)
symmetric?
 YES
 Is it bell shaped?
 More like 2 bells
 Is it single-peaked?
 NO
And this one?
 Is it (more or less)
symmetric?
 NO
 Is it bell shaped?
 YES
 Is it single-peaked?
 YES
68-95-99.7 Rule
 We can use the 68-95-99.7 rule to assess normality as well
 In addition to its other uses in estimating percent to the left/right
 We know that in ANY true normal distribution, 68 percent of the
observations will fall within 1 standard deviation of the mean
 Remember, no real-world distribution is PERFECTLY normal
 But some are close enough approximations, while others are not
 So what we are really doing is assessing whether it is close enough to
think of it as being normal
68-95-99.7 Rule
 Take the data to the right
 29
 12.7
 How would we go about using 68-95-
99.7 rule?
 What would be the first step?
 26.4
 19.4
 18.8
 21.6
 17
 10.3
 23.7
 26.6
68-95-99.7 Rule
 Take the data to the right
 29
 12.7
 How would we go about using 68-95-99.7
rule?
 What would be the first step?
 Calculate the mean
 The mean here is 20.6
 What would we do next?
 26.4
 19.4
 18.8
 21.6
 17
 10.3
 23.7
 26.6
68-95-99.7 Rule
 Take the data to the right
 29
 12.7
 How would we go about using 68-95-99.7
rule?
 What would be the first step?
 Calculate the mean
 The mean here is 20.6
 What would we do next?
 Calculate the standard deviation
 Here it is 6.1
 Then what do we need to do?
 26.4
 19.4
 18.8
 21.6
 17
 10.3
 23.7
 26.6
68-95-99.7 Rule
 How would we go about using 68-95-99.7
rule?
 What would be the first step?
 Calculate the mean
 The mean here is 20.6
 What would we do next?
 Calculate the standard deviation
 Here it is 6.1
 Then what do we need to do?
 Calculate Z-scores for each observation
(see right)
 Now what?
 1.38
 -1.28
 0.95
 -0.19
 -0.29
 0.17
 -0.58
 -1.67
 0.51
 0.99
68-95-99.7 Rule
Calculate Z-scores for each observation (see right)
Now what?
Now we compare to the 68-95-99.7 expectation
Are there about 68% between 0 and 1 standard
deviations from the mean?
 Yes, 7 out of 10 have Z-scores between -1 and 1
 Are there about 95% between -2 and 2?
 Yes, 10 out of 10
 Are there about 99.7% between -3 and 3?
 Yes, 10 out of 10
 This provides us an indication that it is probably normally
distributed














1.38
-1.28
0.95
-0.19
-0.29
0.17
-0.58
-1.67
0.51
0.99
Normal Probability Plots
Which of these do you
think is ‘normal?’
Normal Probability Plots
This one
Not this one
Normal Probability Plots
 Graph the actual values versus the z-scores
 A completely normal distribution will be a perfectly straight
line
 When assessing normality, we will never see something
perfectly straight—if it is close, then we can consider it
normal
Normal Probability Plots
 Here is the normal probability plot of our 10-observation
dataset from earlier. We concluded from the 68-95-99.7 rule
that it was normally distributed. Does it look normal here
too?
2
1.5
1
0.5
0
0
-0.5
-1
-1.5
-2
5
10
15
20
25
30
35
Let’s Try It
 Make a normal probability plot of the following data:
 9, 1, 5, 3, 8, 1
 Should we consider this to be normally distributed?
Let’s Try It
 Should look like this:
1.5
1
0.5
0
0
-0.5
-1
-1.5
2
4
6
8
10
Technology Corner (Page 128)
 Click the STAT button
 Enter your data under L1
 Click 2ND then STAT PLOT (y= button)
 Plot 1 will be highlighted
 Hit enter
 Turn it ON
 Choose option 6 for type
 Data list should be L1
 Data axis should be X
 Hit graph
Assessing Normality
 Which of our three methods do you prefer?
Assessing Normality
 Look at it
 Downside: imprecise—some wiggle room
 Using 68-95-99.7 rule
 Downside: more effective with bigger datasets
 Normal Probability Plots
 Downside: most time-intensive
 But also the most precise
Normal Probability Plots on the AP
Exam
 They will NEVER specifically ask you to make one
 They will NEVER specifically ask you to interpret one
 BUT, they CAN ask you to assess the normality of a certain
dataset
 You could do this using the 68-95-99.7 rule
 But you could also do this by making a normal probability plot
 Whatever makes the most sense to you
Chapter 1 Test (Raw Scores)
Chapter 1 Test Raw Score Statistics
 Mean: 64.85
 Median: 65
 Standard Deviation: 11.24
 Q1: 60.5
 Q3: 71.75
Chapter 1 Test Curve
 This test was curved
 Not scaled!
 First, I took the z-score for each score
 (value minus mean) divided by standard deviation
 I then decided what I wanted the distribution to look like
 I set it up so that the mean was 80, and the highest score was a 100
 This meant using a standard deviation of approximately 8.28
 Using these numbers, I calculated your curved score by adding your (Z-
score times 8.28) to 80. So if your Z-score was exactly 1, you would
have a score of 88.28.
Curved Scores
Then add extra Credit
 This is what shows up in Infinite Campus
 Take the curved score, and add your extra credit value if you
did the summer homework