1. Graphical Methods for Describing Data
Chapters 1 & 3
Graphical Methods for Describing Data

2. What is statistics?
the science of collecting, organizing, analyzing, and drawing conclusions from data

3. Why should one study statistics?
Can dogs help patients with heart failure by reducing stress and anxiety?
To be informed . . .
Extract information from tables, charts and graphs
Follow numerical arguments
Understand the basics of how data should be gathered, summarized, and analyzed to draw statistical conclusions
Examples come from page 2 & 3.
When people take a vacation do they really leave work behind?

4. Why should one study statistics? (continued)
Many companies now require drug screening as a condition of employment. With these screening tests there is a risk of a false-positive reading. Is the risk of a false result acceptable?
To make informed judgments
To evaluate decisions that affect your life
If you choose a particular major, what are your chances of finding a job when you graduate?
Examples come from page 2 & 3.

5. It is variability that makes life interesting!!
What is variability?
Suppose you went into a convenience store to purchase a soft drink. Does every can on the shelf contain exactly 12 ounces?
NO – there may be a little more or less in the various cans due to the variability that is inherent in the filling process.
In fact, variability is almost universal!
The quality, state, or degree of being variable or changeable.
It is variability that makes life interesting!!
Discuss the fact that variability exist in almost everything – provide several examples.

6. If the Shoe Fits ...
The two histograms to the right display the distribution of heights of gymnasts and the distribution of heights of female basketball players. Which is which? Why?
Heights – Figure A
See Example 1.1 for more explanation.
Heights – Figure B

7. If the Shoe Fits ...
Suppose you found a pair of size 6 shoes left outside the locker room. Which team would you go to first to find the owner of the shoes? Why?
Suppose a tall woman (5 ft 11 in) tells you see is looking for her sister who is practicing with a gym. To which team would you send her? Why?
Center & spread

8. The Data Analysis Process
Understand the nature of the problem
Decide what to measure and how to measure it
Collect data
Summarize data and perform preliminary analysis
Perform formal analysis
Interpret results
It is important to have a clear direction before gathering data.
It is important to select and apply the appropriate inferential statistical methods
It is important to carefully define the variables to be studied and to develop appropriate methods for determining their values.
This step often leads to the formulation of new research questions.
It is important to understand how data is collected because the type of analysis that is appropriate depends on how the data was collected!
This initial analysis provides insight into important characteristics of the data.

9. What term would be used to describe “all high school graduates”?
Suppose we wanted to know the average GPA of high school graduates in the nation this year.
We could collect data from all high schools in the nation.
population
What term would be used to describe “all high school graduates”?

10. What do you call it when you collect data about the entire population?
The entire collection of individuals or objects about which information is desired
A census is performed to gather about the entire population
What do you call it when you collect data about the entire population?

11. We could collect data from all high schools in the nation.
GPA Continued:
Suppose we wanted to know the average GPA of high school graduates in the nation this year.
We could collect data from all high schools in the nation.
Why might we not want to use a census here?
Discuss some problems associated with performing a census:
Takes a lot of time
Inaccurate data
Missing data
costly
If we didn’t perform a census, what would we do?

12. Sample
A subset of the population, selected for study in some prescribed manner
What would a sample of all high school graduates across the nation look like?
High school graduates from each state (region), ethnicity, gender, etc.

13. Once we have collected the data, what would we do with it?
GPA Continued:
Suppose we wanted to know the average GPA of high school graduates in the nation this year.
We could collect data from a sample of high schools in the nation.
Once we have collected the data, what would we do with it?
Organize it – graph & make some calculations etc.

14. Descriptive statistics
the methods of organizing & summarizing data
If the sample of high school GPAs contained 1,000 numbers, how could the data be organized or summarized?
Create a graph
State the range of GPAs
Calculate the average GPA

15. Could we use the data from our sample to answer this question?
GPA Continued:
Suppose we wanted to know the average GPA of high school graduates in the nation this year.
We could collect data from a sample of high schools in the nation.
Organize it – graph & make some calculations etc.
Could we use the data from our sample to answer this question?

16. Inferential statistics
involves making generalizations from a sample to a population
Based on the sample, if the average GPA for high school graduates was 3.0, what generalization could be made?
The average national GPA for this year’s high school graduate is approximately 3.0.
Could someone claim that the average GPA for graduates in your local school district is 3.0?
Be sure to sample from the population of interest!!
No. Generalizations based on the results of a sample can only be made back to the population from which the sample came from.

17. The number of wrecks per week at the intersection outside school?
Variable
any characteristic whose value may change from one individual to another
Suppose we wanted to know the average GPA of high school graduates in the nation this year. Define the variable of interest.
Is this a variable . . .
The number of wrecks per week at the intersection outside school?
Give several examples of different variables
The variable of interest is the GPA of high school graduates
YES

18. Data The values for a variable from individual observations 0, 1, 2, …
For this variable . . .
The number of wrecks per week at the intersection outside What could observations be?
0, 1, 2, …

19. Two types of variables
categorical
numerical
discrete
continuous

20. Categorical variables
Qualitative
Identifies basic differentiating characteristics of the population
Can you name any categorical variables?

21. Can you name any numerical variables?
quantitative
observations or measurements take on numerical values
makes sense to average these values
two types - discrete & continuous
Can you name any numerical variables?

22. Discrete (numerical) Isolated points along a number line
usually counts of items

23. Continuous (numerical)
Variable that can be any value in a given interval
usually measurements of something

24. Identify the following variables:
the color of cars in the teacher’s lot
the number of calculators owned by students at your school
the zip code of an individual
the amount of time it takes students to drive to school
the appraised value of homes in your city
Categorical
Discrete numerical
Categorical
Is money a measurement or a count?
Continuous numerical
discrete numerical

25. Classifying variables by the number of variables in a data set
Suppose that the PE coach records the height of each student in his class.
Univariate - data that describes a single characteristic of the population
This is an example of a univariate data

26. Classifying variables by the number of variables in a data set
Suppose that the PE coach records the height and weight of each student in his class.
Bivariate - data that describes two characteristics of the population
This is an example of a bivariate data

27. Classifying variables by the number of variables in a data set
Suppose that the PE coach records the height, weight, number of sit-ups, and number of push-ups for each student in his class.
Multivariate - data that describes more than two characteristics (beyond the scope of this course)
This is an example of a multivariate data

28. Graphs for categorical data

29. Bar Chart When to Use Categorical data How to construct
Draw a horizontal line; write the categories or labels below the line at regularly spaced intervals
Draw a vertical line; label the scale using frequency or relative frequency
Place equal-width rectangular bars above each category label with a height determined by its frequency or relative frequency

30. Bar Chart (continued) What to Look For
Frequently or infrequently occurring categories
Collect the following data and then display the data in a bar chart:
What is your favorite ice cream flavor?
Vanilla, chocolate, strawberry, or other

31. Why MUST we use relative frequencies?
Double Bar Charts
When to Use Categorical data
How to construct
Constructed like bar charts, but with two (or more) groups being compared
MUST use relative frequencies on the vertical axis
MUST include a key to denote the different bars
Why MUST we use relative frequencies?

32. What should you do first?
Each year the Princeton Review conducts a survey of students applying to college and of parents of college applicants. In 2009, 12,715 high school students responded to the question “Ideally how far from home would you like the college you attend to be?” Also, 3007 parents of students applying to college responded to the question “how far from home would you like the college your child attends to be?” Data is displayed in the frequency table below.
What should you do first?
The first step is to calculate the relative frequencies for students and for parents.
Frequency
Ideal Distance
Students
Parents
Less than 250 miles
4450
1594
250 to 500 miles
3942
902
500 to 1000 miles
2416
331
More than 1000 miles
1907
180
Create a comparative bar chart with these data.

33. Relative Frequency
Ideal Distance
Students
Parents
Less than 250 miles
.35
.53
250 to 500 miles
.31
.30
500 to 1000 miles
.19
.11
More than 1000 miles
.15
.06
Found by dividing the frequency by the total number of students
Found by dividing the frequency by the total number of parents
What does this graph show about the ideal distance college should be from home?
See page 97 for a discussion of graph

34. Segmented (or Stacked) Bar Charts
When to Use Categorical data
How to construct
MUST first calculate relative frequencies
Draw a bar representing 100% of the group
Divide the bar into segments corresponding to the relative frequencies of the categories

35. Remember the Princeton survey . . .
Create a segmented bar graph with these data.
First draw a bar that represents 100% of the students who answered the survey.
Relative Frequency
Ideal Distance
Students
Parents
Less than 250 miles
.35
.53
250 to 500 miles
.31
.30
500 to 1000 miles
.19
.11
More than 1000 miles
.15
.06

36. Next, divide the bar into segments.
Relative Frequency
Ideal Distance
Students
Parents
Less than 250 miles
.35
.53
250 to 500 miles
.31
.30
500 to 1000 miles
.19
.11
More than 1000 miles
.15
.06
Notice that this segmented bar chart displays the same relationship between the opinions of students and parents concerning the ideal distance that college is from home as the double bar chart does.
First draw a bar that represents 100% of the students who answered the survey.
Relative frequency
Students
Next, divide the bar into segments.
Do the same thing for parents – don’t forget a key denoting each category
Less than 250 miles
250 to 500 miles
500 to 1000 miles
More than 1000 miles
Parents

37. Pie (Circle) Chart When to Use Categorical data How to construct
Draw a circle to represent the entire data set
Calculate the size of each “slice”:
Relative frequency × 360°
Using a protractor, mark off each slice
To describe
– comment on which category had the largest proportion or smallest proportion

38. Create a pie chart for these data.
Typos on a résumé do not make a very good impression when applying for a job. Senior executives were asked how many typos in a résumé would make them not consider a job candidate. The resulting data are summarized in the table below.
Number of Typos
Frequency
Relative Frequency 1 60
.40 2 54
.36 3 21
.14
4 or more 10
.07
Don’t know 5
.03
Create a pie chart for these data.

39. First draw a circle to represent the entire data set.
Number of Typos
Frequency
Relative Frequency 1 60
.40 2 54
.36 3 21
.14
4 or more 10
.07
Don’t know 5
.03
What does this pie chart tell us about the number of typos occurring in résumés before the applicant would not be considered for a job?
First draw a circle to represent the entire data set.
Next, calculate the size of the slice for “1 typo”
.40×360º =144º
Draw that slice.
Repeat for each slice.
Here is the completed pie chart created using Minitab.
See Page 98 for this example.

40. Graphs for numerical data

41. Dotplot How to construct When to Use Small numerical data sets
Draw a horizontal line and mark it with an appropriate numerical scale
Locate each value in the data set along the scale and represent it by a dot. If there are two are more observations with the same value, stack the dots vertically

42. Dotplot (continued) What to Look For
The representative or typical value
The extent to which the data values spread out
The nature of the distribution along the number line
The presence of unusual values
Collect the following data and then display the data in a dotplot:
How many body piercings do you have?

43. How to describe a numerical, univariate graph
Do after Features of Distributions Activity

44. What strikes you as the most distinctive difference among the distributions of exam scores in classes A, B, & C ?
Adapted from “Workshop Statistics” by Rossman

45. The mean and/or median is typically reported rather than the mode.
1. Center
discuss where the middle of the data falls
three measures of central tendency
mean, median, & mode
The mean and/or median is typically reported rather than the mode.

46. What strikes you as the most distinctive difference among the distributions of scores in classes D, E, & F?
Adapted from “Workshop Statistics” by Rossman

47. 2. Spread discuss how spread out the data is
refers to the variability in the data
Measure of spread are
Range, standard deviation, IQR
Remember,
Range = maximum value – minimum value
Standard deviation & IQR will be discussed in Chapter 4

48. What strikes you as the most distinctive difference among the distributions of exam scores in classes G, H, & I ?
Adapted from “Workshop Statistics” by Rossman

49. The following slides will discuss these shapes.
refers to the overall shape of the distribution
The following slides will discuss these shapes.

50. Symmetrical
1. Collect data by rolling two dice and recording the sum of the two dice. Repeat three times.
2. Plot your sums on the dotplot on the board.
3. What shape does this distribution have?
refers to data in which both sides are (more or less) the same when the graph is folded vertically down the middle
bell-shaped is a special type
has a center mound with two sloping tails

51. Uniform
1. Collect data by rolling a single die and recording the number rolled. Repeat five times.
2. Plot your numbers on the dotplot on the board.
3. What shape does this distribution have?
refers to data in which every class has equal or approximately equal frequency
To help remember the name for this shape, picture soldier standing in straight lines. What are they wearing?

52. Skewed
1. Collect data finding the age of five coins in circulation (current year minus year of coin) and record
2. Plot the ages on the dotplot on the board.
3. What shape does this distribution have?
Name a variable with a distribution that is skewed left.
refers to data in which one side (tail) is longer than the other side
the direction of skewness is on the side of the longer tail
Example of a left skewed distribution, hopefully, is the distribution of grades on the first statistics test.
The directions are right skewed or left skewed.

53. Bimodal (multi-modal)
Suppose collect data on the time it takes to drive from San Luis Obispo, California to Monterey, California. Some people may take the inland route (approximately 2.5 hours) while others may take the coastal route (between 3.5 and 4 hours).
What shape would this distribution have?
refers to the number of peaks in the shape of the distribution
Bimodal would have two peaks
Multi-modal would have more than two peaks
Bimodal distributions can occur when the data set consist of observations from two different kinds of individuals or objects.
See page 127 for an example of a data set that is bimodal.
What would a distribution be called if it had ONLY one peak?
Unimodal

54. 3. Shape refers to the overall shape of the distribution
symmetrical, uniform, skewed, or bimodal

55. What strikes you as the most distinctive difference among the distributions of exam scores in class J ?
Adapted from “Workshop Statistics” by Rossman

56. 4. Unusual occurrences
Outlier - value that lies away from the rest of the data
Gaps
Clusters

57. 5. In context
You must write your answer in reference to the context in the problem, using correct statistical vocabulary and using complete sentences!

58. Dotplot (continued) What to Look For
The representative or typical value
The extent to which the data values spread out
The nature of the distribution along the number line
The presence of unusual values
Collect the following data and then display the data in a dotplot:
How many body piercings do you have?
Describe the distribution of the number of body piercings the class has.

59. Numerical Graphs Continued

60. Stem-and-Leaf Displays
When to Use Univariate numerical data
How to construct
Select one or more of the leading digits for the stem
List the possible stem values in a vertical column
Record the leaf for each observation beside each corresponding stem value
Indicate the units for stems and leaves in a key or legend
To describe
– comment on the center, spread, and shape of the distribution and if there are any unusual features
Each number is split into two parts:
Stem – consists of the first digit(s)
Leaf - consists of the final digit(s)
Can also create comparative stem-and-leaf displays
Remember the data set collected in Chapter 1 – how many piercings do you have? Would a stem-and-leaf display be a good graph for this distribution? Why or why not?
Use for small to moderate sized data sets. Doesn’t work well for large data sets.
Be sure to list every stem from the smallest to the largest value
If you have a long lists of leaves behind a few stems, you can split stems in order to spread out the distribution.

61. Create a stem-and-leaf display with this data?
The following data are price per ounce for various brands of different brands of dandruff shampoo at a local grocery store.
Create a stem-and-leaf display with this data?
What would an appropriate stem be?
For the observation of “0.32”, write the 2 behind the “3” stem.
List the stems vertically
The median price per ounce for dandruff shampoo is $0.285, with a range of $ The distribution is positively skewed with an outlier at $0.54.
Stem
Leaf 1 2 3 4 5
Continue recording each leaf with the corresponding stem
Describe this distribution. 7 1 9 8 3 2 6 4

62. Notice that now you can see the shape of this distribution.
The Census Bureau projects the median age in 2030 for the 50 states and Washington D.C. A stem-and-leaf display is shown below.
Notice that now you can see the shape of this distribution.
We use L for lower leaf values (0-4) and H for higher leaf values (5-9).
Notice that you really cannot see a distinctive shape for this distribution due to the long list of leaves
We can split the stems in order to better see the shape of the distribution.

63. Let’s truncate the leaves to the unit place.
The median percentage of primary-school-aged children enrolled in school is larger for countries in Northern Africa than in Central Africa, but the ranges are the same. The distribution for countries in Northern Africa is strongly negatively skewed, but the distribution for countries in Central Africa is approximately symmetrical.
The following is data on the percentage of primary-school-aged children who are enrolled in school for 19 countries in Northern Africa and for 23 countries in Central African.
Northern Africa
Central Africa
Let’s truncate the leaves to the unit place.
“4.6” becomes “4”
Create a comparative stem-and-leaf display.
Be sure to use comparative language when describing these distributions!
What is an appropriate stem?

64. Constructed differently for discrete versus continuous data
Histograms
When to Use Univariate numerical data
How to construct Discrete data
Draw a horizontal scale and mark it with the possible values for the variable
Draw a vertical scale and mark it with frequency or relative frequency
Above each possible value, draw a rectangle centered at that value with a height corresponding to its frequency or relative frequency
To describe
– comment on the center, spread, and shape of the distribution and if there are any unusual features
Constructed differently for discrete versus continuous data
For comparative histograms – use two separate graphs with the same scale on the horizontal axis

65. Create a histogram for the number of partners of the queen bees.
Queen honey bees mate shortly after they become adults. During a mating flight, the queen usually takes several partners, collecting sperm that she will store and use throughout the rest of her life. A study on honey bees provided the following data on the number of partners for 30 queen bees.
Create a histogram for the number of partners of the queen bees.

66. What do you notice about the shapes of these two histograms?
Draw a rectangle above each value with a height corresponding to the frequency.
First draw a horizontal axis, scaled with the possible values of the variable of interest.
Next draw a vertical axis, scaled with frequency or relative frequency.
Suppose we use relative frequency instead of frequency on the vertical axis.
What do you notice about the shapes of these two histograms?

67. This is the type of histogram that most students are familiar with.
Histograms
When to Use Univariate numerical data
How to construct Continuous data
Mark the boundaries of the class intervals on the horizontal axis
Draw a vertical scale and mark it with frequency or relative frequency
Draw a rectangle directly above each class interval with a height corresponding to its frequency or relative frequency
To describe
– comment on the center, spread, and shape of the distribution and if there are any unusual features
This is the type of histogram that most students are familiar with.

68. Notice the common scale on the horizontal axis
A study examined the length of hours spent watching TV per day for a sample of children age 1 and for a sample of children age 3. Below are comparative histograms.
The median number of hours spent watching TV per day was greater for the 1-year-olds than for the 3-year-olds. The distribution for the 3-year-olds was more strongly skewed right than the distribution for the 1-year-olds, but the two distributions had similar ranges.
Children Age 1
Children Age 3
Notice the common scale on the horizontal axis
Write a few sentences comparing the distributions.
See pages

69. Cumulative Relative Frequency Plot
When to use
- used to answer questions about percentiles.
How to construct
- Mark the boundaries of the intervals on the horizontal axis
- Draw a vertical scale and mark it with relative frequency
- Plot the point corresponding to the upper end of each interval with its cumulative relative frequency, including the beginning point
- Connect the points.
Percentiles are a value with a given percent of observations at or below that value.

70. Cumulative relative frequency
The National Climatic Center has been collecting weather data for many years. The annual rainfall amounts for Albuquerque, New Mexico from 1950 to 2008 were used to create the frequency distribution below.
Find the cumulative relative frequency for each interval
Annual Rainfall
(in inches)
Relative frequency
Cumulative relative frequency
4 to <5
0.052
5 to <6
0.103
6 to <7
0.086
7 to <8
8 to <9
0.172
9 to <10
0.069
10 to < 11
0.207
11 to <12
12 to <13
13 to <14
0.052 +
0.155 +
0.241
Continue this pattern to complete the table

71. Cumulative relative frequency
The National Climatic Center has been collecting weather data for many years. The annual rainfall amounts for Albuquerque, New Mexico from 1950 to 2008 were used to create the frequency distribution below.
To create a cumulative relative frequency plot, graph a point for the upper value of the interval and the cumulative relative frequency
In the context of this problem, explain the meaning of this value.
Annual Rainfall
(in inches)
Relative frequency
Cumulative relative frequency
4 to <5
0.052
5 to <6
0.103
0.155
6 to <7
0.086
0.241
7 to <8
0.344
8 to <9
0.172
0.516
9 to <10
0.069
0.585
10 to < 11
0.207
0.792
11 to <12
0.895
12 to <13
0.947
13 to <14
0.999
Why isn’t this value one (1)?
Plot a point for each interval.
Plot a starting point at (4,0).
Connect the points.
In the context of this problem, explain the meaning of this value.

72. What proportion of years had rainfall amounts that were 9
What proportion of years had rainfall amounts that were 9.5 inches or less?
Rainfall
Cumulative relative frequency
Approximately 0.55

73. Approximately 30% of the years had annual rainfall less than what amount?
Cumulative relative frequency
Approximately 7.5 inches

74. Which interval of rainfall amounts had a larger proportion of years –
9 to 10 inches or
10 to 11 inches?
Explain
Rainfall
Cumulative relative frequency
The interval 10 to 11 inches, because its slope is steeper, indicating a larger proportion occurred.

75. Displaying Bivariate Numerical Data

76. Scatterplots are discussed in much greater depth in Chapter 5.
When to Use Bivariate numerical data
How to construct
- Draw a horizontal scale and mark it with appropriate values of the independent variable
- Draw a vertical scale and mark it appropriate values of the dependent variable
- Plot each point corresponding to the observations
To describe
- comment the relationship between the variables
Scatterplots are discussed in much greater depth in Chapter 5.

77. Time Series Plots When to Use How to construct To describe
- measurements collected over time at regular intervals
How to construct
- Draw a horizontal scale and mark it with appropriate values of time
- Draw a vertical scale and mark it appropriate values of the observed variable
- Plot each point corresponding to the observations and connect
To describe
- comment on any trends or patterns over time
Can be considered bivariate data where the y-variable is the variable measured and the x-variable is time

78. The accompanying time-series plot of movie box office totals (in millions of dollars) over 18 weeks in the summer for 2001 and 2002 appeared in USA Today (September 3, 2002).
Describe any trends or patterns that you see.