2.3 Graphs that enlighten and graphs that deceive

Elementary Statistics
Chapter 2:
Exploring Data with
Tables and Graphs
2.3 Graphs that
Enlighten and Graphs
that Deceive
1

Chapter 2:
Exploring Data with Tables and Graphs
2.1 Frequency Distributions for Organizing and
Summarizing Data
2.2 Histograms
2.3 Graphs that Enlighten and Graphs that Deceive
2.4 Scatterplots, Correlation, and Regression
2
Objectives:
1. Organize data using a frequency distribution.
2. Represent data in frequency distributions graphically using histograms, frequency polygons, and ogives.
3. Represent data using bar graphs, Pareto charts, time series graphs, and pie graphs.
4. Draw and interpret a stem and leaf plot.
5. Draw and interpret a scatter plot for a set of paired data.

Recall: 2.1 Frequency Distributions for Organizing and Summarizing Data
Data collected in original form is called raw data.
Frequency Distribution (or Frequency Table)
A frequency distribution is the organization of raw data in table form, using
classes and frequencies. It Shows how data are partitioned among several
categories (or classes) by listing the categories along with the number
(frequency) of data values in each of them.
Nominal- or ordinal-level data that can be placed in categories is organized in
categorical frequency distributions.
Lower class limits: The smallest numbers that can belong to each of the
different classes
Upper class limits: The largest numbers that can belong to each of the
different classes
Class boundaries: The numbers used to separate the classes, but without the
gaps created by class limits
Class midpoints: The values in the middle of the classes Each class midpoint
can be found by adding the lower class limit to the upper class limit and
dividing the sum by 2.
Class width: The difference between two consecutive lower class limits in a
frequency distribution
Procedure for Constructing a
Frequency Distribution
1. Select the number of classes,
usually between 5 and 20.
2. Calculate the class width: 𝑊 =
𝑀𝑎𝑥−𝑀𝑖𝑛
# 𝑜𝑓 𝑐𝑙𝑎𝑠𝑠𝑒𝑠
and round up
accordingly.
3. Choose the value for the first
lower class limit by using either
the minimum value or a
convenient value below the
minimum.
4. Using the first lower class limit
and class width, list the other
lower class limits.
5. List the lower class limits in a
vertical column and then
determine and enter the upper
class limits.
6. Take each individual data value
and put a tally mark in the
appropriate class. Add the tally
marks to get the frequency.
3

Normal Distribution:
Because this histogram is roughly
bell-shaped, we say that the data
have a normal distribution.
4
Skewness:
A distribution of data is skewed if it is not symmetric and extends
more to one side than to the other.
Data skewed to the right
(positively skewed) have a
longer right tail.
Data skewed to the left
(negative skewed) have a
longer left tail.
Recall: 2.2 Histograms
Histogram: A graph consisting of bars of equal width drawn adjacent to each other (unless there are gaps in the data)
The horizontal scale represents classes of quantitative data values, and the vertical scale represents frequencies. The
heights of the bars correspond to frequency values.
Important Uses of a Histogram
• Visually displays the shape of the distribution of the data
• Shows the location of the center of the data
• Shows the spread of the data & Identifies outliers

Key Concept
Common graphs that foster understanding of data and some graphs that are
deceptive because they create impressions about data that are somehow
misleading or wrong.
2.3 Graphs that Enlighten and Graphs that Deceive
5
Dotplots & its Features:
A graph of quantitative data in which each data value is plotted as a point (or dot) above a horizontal
scale of values. Dots representing equal values are stacked.
Displays the shape of distribution of data.
It is usually possible to recreate the original list of data values.
Example 1 Construct a Dotplot and find an outlier if any.
Number 36 seems to be an outlier.

Stemplots (or stem-and-leaf plot)
Represents quantitative data by separating each value into two parts: the
stem (such as the leftmost digit) and the leaf (such as the rightmost digit).
Features of a Stemplot
Shows the shape of the distribution of the data.
Retains the original data values.
The sample data are sorted (arranged in order).
6
Graphs that Enlighten
Example 2
Construct a Stemplot.

Time-Series Graph
A graph of time-series data, which are quantitative data that have been collected at different
points in time, such as monthly or yearly
Feature of a Time-Series Graph: Reveals information about trends over time.
7
Example 3 Construct a Time-Series Graph.
From 1990 to 2011, there seems to be upward trend that appears
to be leveling off in recent years. It would help close the gender
gap in earning.

Bar Graphs: A graph of bars of equal width to show frequencies of categories of categorical
(or qualitative) data. The bars may or may not be separated by small gaps.
Feature of a Bar Graph: Shows the relative distribution of categorical data so that it is easier
to compare the different categories.
8
Pareto Charts: A Pareto chart is a bar graph for categorical data, with the
added condition that the bars are arranged in descending order according
to frequencies, so the bars decrease in height from left to right.
Features of a Pareto Chart: Shows the relative distribution of categorical data
so that it is easier to compare the different categories.
Draws attention to the more important categories.
Yes, it
does.
Example 4 Construct the Pareto Chart.

Pie Charts
A very common graph that depicts categorical data as slices of a circle, in which the size of
each slice is proportional to the frequency count for the category
Feature of a Pie Chart: Each sector =→ 𝟑𝟔𝟎 𝟎
× %
Shows the distribution of categorical data in a commonly used format.
9
Example 5
Construct the Pie Chart.

Frequency Polygon
A graph using line segments connected to points located directly above class midpoint values
A frequency polygon is very similar to a histogram, but a frequency polygon uses line segments instead of bars.
Relative Frequency Polygon
A variation of the basic frequency polygon is the relative frequency polygon, which uses relative frequencies (proportions
or percentages) for the vertical scale.
10

Relative F
f/n
11
Graphs that EnlightenExample 6
Construct a histogram, frequency polygon, and ogive
using relative frequencies for the distribution (shown
here) of the miles that 20 randomly selected runners
ran during a given week.
Class
Boundaries
F
5.5 - 10.5
10.5 - 15.5
15.5 - 20.5
20.5 - 25.5
25.5 - 30.5
30.5 - 35.5
35.5 - 40.5
1
2
3
5
4
3
2
1/20 =
2/20 =
3/20 =
5/20 =
4/20 =
3/20 =
2/20 =
f = 20 rf = 1.00
0.05
0.10
0.15
0.25
0.20
0.15
0.10
Midpoi
nts
8
13
18
23
28
33
38

Relative F
f/n
12
Graphs that EnlightenExample 6 Continued
Construct a histogram, frequency polygon, and ogive
using relative frequencies for the distribution (shown
here) of the miles that 20 randomly selected runners
ran during a given week.
Class
Boundaries
F
5.5 - 10.5
10.5 - 15.5
15.5 - 20.5
20.5 - 25.5
25.5 - 30.5
30.5 - 35.5
35.5 - 40.5
1
2
3
5
4
3
2
1/20 =
2/20 =
3/20 =
5/20 =
4/20 =
3/20 =
2/20 =
f = 20 rf = 1.00
0.05
0.10
0.15
0.25
0.20
0.15
0.10
Midpoi
nts
8
13
18
23
28
33
38

13
Construct an ogive:
It’s a line graph that depicts cumulative frequencies
Class
Boundaries
F
5.5 - 10.5
10.5 - 15.5
15.5 - 20.5
20.5 - 25.5
25.5 - 30.5
30.5 - 35.5
35.5 - 40.5
1
2
3
5
4
3
2
1/20 =
2/20 =
3/20 =
5/20 =
4/20 =
3/20 =
2/20 =
f = 20 rf = 1.00
0.05
0.10
0.15
0.25
0.20
0.15
0.10
Relative F
f/n
Class
Boundaries
Cumulative F
5.5 - 10.5
5.5 - 15.5
5.5 - 20.5
5.5 - 25.5
5.5 - 30.5
5.5 - 35.5
5.5 - 40.5
1
3
6
11
15
18
20
Cum. Rel.
Frequency
1/20 =
3/20 =
6/20 =
11/20 =
15/20 =
18/20 =
20/20 =
0.05
0.15
0.30
0.55
0.75
0.90
1.00

14
Construct an ogive:
It’s a line graph that depicts cumulative frequencies
Ogives use upper class boundaries and cumulative frequencies or
cumulative relative frequencies of the classes.
Class
Boundaries
Cumulative F
5.5 - 10.5
5.5 - 15.5
5.5 - 20.5
5.5 - 25.5
5.5 - 30.5
5.5 - 35.5
5.5 - 40.5
1
3
6
11
15
18
20
Cum. Rel.
Frequency
1/20 =
3/20 =
6/20 =
11/20 =
15/20 =
18/20 =
20/20 =
0.05
0.15
0.30
0.55
0.75
0.90
1.00 What percent of runners
ran 22 miles or less? About 41%

Nonzero Vertical Axis
A common deceptive graph involves using a vertical scale that starts at some value
greater than zero to exaggerate differences between groups.
15
Graphs that Deceive
Always examine a graph carefully to see whether a vertical axis begins at
some point other than zero so that differences are exaggerated.

Pictographs
Drawings of objects, called pictographs, are often misleading. Data that are one-dimensional in
nature (such as budget amounts) are often depicted with two-dimensional objects (such as dollar bills)
or three-dimensional objects (such as stacks of coins, homes, or barrels).
By using pictographs, artists can create false impressions that grossly distort differences by using
these simple principles of basic geometry:
When you double each side of a square, its area doesn’t merely double; it increases by a factor of four.
When you double each side of a cube, its volume doesn’t merely double; it increases by a factor of eight.
16
Graphs that Deceive
Misleading. Depicts one-dimensional
data with three-dimensional boxes.
Last box is 64 times as large as first
box, but income is only 4 times as
large.
Fair, objective,
clear from any
distracting
features.

2.3 Graphs that enlighten and graphs that deceive

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2.3 Graphs that enlighten and graphs that deceive