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Chapter 7: graphing data,
Categorical Data:
a) pie chart/ pie graph
Pie Chart of High
C ategory
No
Yes
Yes
41.1%
No
58.9%
b) bar chart/ bar graph (height represents counts)
Chart of Gender, High
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Count
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20
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0
High
Gender
No
Yes
Female
No
Yes
Male
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Quantitative or Measurement Data:
a) Histogram – i.e. Skewed right, left, or symmetric
Histogram of Pct
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12
Frequency
10
8
6
4
2
0
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Pct
b) Box plot – Use 5 number summary
Min, Q1, Median, Q3 (IQR), Max, Recall finding outliers by:
Q1 - 1.5*IQR and Q3 + 1.5*IQR where IQR is found by Q3 - Q1
On a boxplot and outliers will be symbolized by an asterisk, *
Boxplot of Pct
1.0
0.9
0.8
Pct
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0.6
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0.3
0.2
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c) Stem and Leaf: breaks data into “stem” and “leaves”
Example: 10 exam scores: 50, 55, 75, 75, 80, 85, 88, 90, 95, 100
Stem Leaf
5|
0,5
6|
7|
5,5
8|
0,5,8
9|
0,5
10|
0
Median is the halfway point
d) If we want to look at 2 Quantitative variables together consider a scatter plot
you can see a relationship between the two.
Scatterplot of Exam 1 vs Quizzes Average
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90
Exam 1
80
70
60
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40
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0
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40
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Quizzes A verage
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100
What do outliers do?
Quantitative Data:
- min, max, Q1, median, Q3, mean, standard deviation, variance(= Standard
Deviation Squared), IQR
- Skewed right : mean larger
- Skewed left: mean smaller
- Mean is more greatly affected by outliers than is the median
- Anything associated with mean is affected = Standard Deviation, Variance – both
mean-driven
- Q1 and Q3 are median-driven
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Chapter 9
4 common graphing errors
1) Not labeling an axis
2) Misleading units of measurement
ex: Stamps not graphed according to how much the worth of a dollar is
3) Not starting at 0
ex: heights of football players, drinking increase – the picture is misleading
4) Changes in axis labeling
ex: hurricane increasing

Look in LESSONS, under SPECIAL TOPICS, under GRAPHING ERROR
EXAMPLES*
If we want to look at 2 Quantitative variables together, then consider a scatter plot you
can see a relationship between the two
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