1. Chapter 1: Exploring Data
Section 1.2
Displaying Quantitative Data with Graphs
The Practice of Statistics, 4th edition - For AP*
STARNES, YATES, MOORE

2. Chapter 1 Exploring Data
Introduction: Data Analysis: Making Sense of Data
1.1 Analyzing Categorical Data
1.2 Displaying Quantitative Data with Graphs
1.3 Describing Quantitative Data with Numbers

3. Section 1.2 Displaying Quantitative Data with Graphs
Learning Objectives
After this section, you should be able to…
CONSTRUCT and INTERPRET dotplots, stemplots, and histograms
DESCRIBE the shape of a distribution
COMPARE distributions
USE histograms wisely

4. Displaying Quantitative Data
Examining the Distribution of a Quantitative Variable
The purpose of a graph is to help us understand the data. After you make a graph, always ask, “What do I see?”
Displaying Quantitative Data
How to Examine the Distribution of a Quantitative Variable
In any graph, look for the overall pattern and for striking departures from that pattern.
Describe the overall pattern of a distribution by its:
Shape
Outlier
Center
Spread
Note individual values that fall outside the overall pattern. These departures are called outliers.
Don’t forget your SOCS!

5. Displaying Quantitative Data
Describing Shape
When you describe a distribution’s shape, concentrate on the main features. Look for rough symmetry or clear skewness.
Displaying Quantitative Data
Definitions:
A distribution is roughly symmetric if the right and left sides of the graph are approximately mirror images of each other.
A distribution is skewed to the right (right-skewed) if the right side of the graph (containing the half of the observations with larger values) is much longer than the left side.
It is skewed to the left (left-skewed) if the left side of the graph is much longer than the right side.
Symmetric
Skewed-left
Skewed-right

6. Types of Shape:
Symmetric Skewed Right
Skewed Left
Shape

7. Types of Shape:
Unimodal Bimodal
Uniform
Shape

8. Example Smart Phone Battery Life
Here is the estimated battery life for each of 9 different smart phones (in minutes). Make a dotplot of the data and describe what you see.
Example
Smart Phone
Battery Life (in mins)
Apple iPhone
300
Motorola Droid
385
Plam Pre
Blackberry Bold
360
Blackberry Storm
330
Motorola Cliq
Samsung Moment
Blackberry Tour
HTC Droid
460

9. Displaying Quantitative Data
Comparing Distributions
Some of the most interesting statistics questions involve comparing two or more groups.
Always discuss shape, center, spread, and possible outliers whenever you compare distributions of a quantitative variable.
Displaying Quantitative Data
Example: Energy Cost: Top vs. Bottom Freezers
How do the annual energy costs (in dollars) compare for refrigerators with top freezers and refrigerators with bottom freezers? The data below is from the May 2010 issue of Consumer Reports.
Compare the distributions of energy cost for these two freezers. Don’t forget your SOCS!

10. Displaying Quantitative Data
Stemplots (Stem-and-Leaf Plots)
Another simple graphical display for small data sets is a stemplot. Stemplots give us a quick picture of the distribution while including the actual numerical values.
Displaying Quantitative Data
How to Make a Stemplot
Separate each observation into a stem (all but the final digit) and a leaf (the final digit).
Write all possible stems from the smallest to the largest in a vertical column and draw a vertical line to the right of the column.
Write each leaf in the row to the right of its stem.
Arrange the leaves in increasing order out from the stem.
Provide a key that explains in context what the stems and leaves represent.

11. Displaying Quantitative Data
Stemplots (Stem-and-Leaf Plots)
These data represent the responses of 20 female AP Statistics students to the question, “How many pairs of shoes do you have?” Construct a stemplot.
Displaying Quantitative Data 50 26 31 57 19 24 22 23 38 13 34 30 49 15 51
Stems 1 2 3 4 5
Add leaves
4 9
Order leaves
4 9
Add a key
Key: 4|9 represents a female student who reported having 49 pairs of shoes.

12. Displaying Quantitative Data
Types of Stem Plots:
Splitting Stems and Back-to-Back Stemplots
When data values are “bunched up”, we can get a better picture of the distribution by splitting stems.
Two distributions of the same quantitative variable can be compared using a back-to-back stemplot with common stems.
Displaying Quantitative Data
Females
Males 50 26 31 57 19 24 22 23 38 13 34 30 49 15 51 14 7 6 5 12 38 8 10 11 4 22 35 1 2 3 4 5
Females
333 95
4332 66
410 8 9
100 7
Males
0 4 1
2 2 2 3
3 58 4 5
“split stems”
Key: 4|9 represents a student who reported having 49 pairs of shoes.

13. Back-to-Back Stemplots Example
Try the example from your notes on your own.
Which gender is taller, males or females? A sample of 14-year olds from the United Kingdom was randomly selected using the CensusatSchool website. Here are the heights of the students (in cm). Make a back-to-back stemplot and compare the distributions.
Male: 154, 157, 187, 163, 167, 159, 169, 162, 176, 177, 151, 175, 174, 165, 165, 183, 180
Female: 160, 169, 152, 167, 164, 163, 160, 163, 169, 157, 158, 153, 161, 165, 165, 159, 168, 153, 166, 158, 158, 166
Back-to-Back Stemplots Example

14. Answers Back-to-back Stemplot 9 7 4 1 15 2 3 3 7 8 8 8 9

15. Displaying Quantitative Data
Histograms
Quantitative variables often take many values. A graph of the distribution may be clearer if nearby values are grouped together.
The most common graph of the distribution of one quantitative variable is a histogram.
Displaying Quantitative Data
How to Make a Histogram
Divide the range of data into classes of equal width.
Find the count (frequency) or percent (relative frequency) of individuals in each class.
Label and scale your axes and draw the histogram. The height of the bar equals its frequency. Adjacent bars should touch, unless a class contains no individuals.

16. Displaying Quantitative Data
Example: The following table presents the average points scored per game (PPG) for the 30 NBA teams in the 2009–2010 regular season. Make a histogram of the distribution.
Displaying Quantitative Data

17. Displaying Quantitative Data
Using Histograms Wisely
Here are several cautions based on common mistakes students make when using histograms.
Displaying Quantitative Data
Cautions
Don’t confuse histograms and bar graphs.
Don’t use counts (in a frequency table) or percents (in a relative frequency table) as data.
Use percents instead of counts on the vertical axis when comparing distributions with different numbers of observations.
Just because a graph looks nice, it’s not necessarily a meaningful display of data.

18. Section 1.2 Displaying Quantitative Data with Graphs
Summary
In this section, we learned that…
You can use a dotplot, stemplot, or histogram to show the distribution of a quantitative variable.
When examining any graph, look for an overall pattern and for notable departures from that pattern. Describe the shape, center, spread, and any outliers. Don’t forget your SOCS!
Some distributions have simple shapes, such as symmetric or skewed. The number of modes (major peaks) is another aspect of overall shape.
When comparing distributions, be sure to discuss shape, center, spread, and possible outliers.
Histograms are for quantitative data, bar graphs are for categorical data. Use relative frequency histograms when comparing data sets of different sizes.

19. Looking Ahead… In the next Section…
We’ll learn how to describe quantitative data with numbers.
Mean and Standard Deviation
Median and Interquartile Range
Five-number Summary and Boxplots
Identifying Outliers
We’ll also learn how to calculate numerical summaries with technology and how to choose appropriate measures of center and spread.