2.2: Display Data
 Last updated
 Save as PDF
 Page ID
 14604
\( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)
\( \newcommand{\vecd}[1]{\overset{\!\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)
\( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)
( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)
\( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)
\( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\ #1 \}\)
\( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)
\( \newcommand{\Span}{\mathrm{span}}\)
\( \newcommand{\id}{\mathrm{id}}\)
\( \newcommand{\Span}{\mathrm{span}}\)
\( \newcommand{\kernel}{\mathrm{null}\,}\)
\( \newcommand{\range}{\mathrm{range}\,}\)
\( \newcommand{\RealPart}{\mathrm{Re}}\)
\( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)
\( \newcommand{\Argument}{\mathrm{Arg}}\)
\( \newcommand{\norm}[1]{\ #1 \}\)
\( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)
\( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)
\( \newcommand{\vectorA}[1]{\vec{#1}} % arrow\)
\( \newcommand{\vectorAt}[1]{\vec{\text{#1}}} % arrow\)
\( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)
\( \newcommand{\vectorC}[1]{\textbf{#1}} \)
\( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)
\( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)
\( \newcommand{\vectE}[1]{\overset{\!\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)
\( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)
\( \newcommand{\vecd}[1]{\overset{\!\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)
\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left#1\right}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Once you have a set of data, you will need to organize it so that you can analyze how frequently each datum occurs in the set. However, when calculating the frequency, you may need to round your answers so that they are as precise as possible.
Frequency
Twenty students were asked how many hours they worked per day. Their responses, in hours, are as follows: 5; 6; 3; 3; 2; 4; 7; 5; 2; 3; 5; 6; 5; 4; 4; 3; 5; 2; 5; 3.
Table \(\PageIndex{1}\) lists the different data values in ascending order and their frequencies.
Data value  Frequency 

2  3 
3  5 
4  3 
5  6 
6  2 
7  1 
Table \(\PageIndex{1}\) Frequency Table of Student Work Hours
A frequency is the number of times a value of the data occurs. According to Table \(\PageIndex{1}\), there are three students who work 2 hours, five students who work 3 hours, and so on. The sum of the values in the frequency column, 20, represents the total number of students included in the sample.
A relative frequency is the ratio (fraction or proportion) of the number of times a value of the data occurs in the set of all outcomes to the total number of outcomes. To find the relative frequencies, divide each frequency by the total number of students in the sample–in this case, 20. Relative frequencies can be written as fractions, percents, or decimals.
Data value  Frequency  Relative frequency 

2  3  \(\frac{3}{20}\) or 0.15 
3  5  \(\frac{5}{20}\) or 0.25 
4  3  \(\frac{3}{20}\) or 0.15 
5  6  \(\frac{6}{20}\) or 0.30 
6  2  \(\frac{2}{20}\) or 0.10 
7  1  \(\frac{1}{20}\) or 0.05 
Table \(\PageIndex{2}\) Frequency Table of Student Work Hours with Relative Frequencies
The sum of the values in the relative frequency column of Table \(\PageIndex{2}\) is \(\frac{20}{20}\), or 1.
Cumulative relative frequency is the accumulation of the previous relative frequencies. To find the cumulative relative frequencies, add all the previous relative frequencies to the relative frequency for the current row, as shown in Table \(\PageIndex{3}\).
Data value  Frequency  Relative frequency  Cumulative relative frequency 

2  3  \(\frac{3}{20}\) or 0.15  0.15 
3  5  \(\frac{5}{20}\) or 0.25  0.15 + 0.25 = 0.40 
4  3  \(\frac{3}{20}\) or 0.15  0.40 + 0.15 = 0.55 
5  6  \(\frac{6}{20}\) or 0.30  0.55 + 0.30 = 0.85 
6  2  \(\frac{2}{20}\) or 0.10  0.85 + 0.10 = 0.95 
7  1  \(\frac{1}{20}\) or 0.05  0.95 + 0.05 = 1.00 
Table \(\PageIndex{3}\) Frequency Table of Student Work Hours with Relative and Cumulative Relative Frequencies
The last entry of the cumulative relative frequency column is one, indicating that one hundred percent of the data has been accumulated.
NOTE
Because of rounding, the relative frequency column may not always sum to one, and the last entry in the cumulative relative frequency column may not be one. However, they each should be close to one.
Table \(\PageIndex{4}\) represents the heights, in inches, of a sample of 100 male semiprofessional soccer players.
Heights (inches)  Frequency  Relative frequency  Cumulative relative frequency 

59.95–61.94  5  \(\frac{5}{10}\) = 0.05  0.05 
61.95–63.94  3  \(\frac{3}{100}\) = 0.03  0.05 + 0.03 = 0.08 
63.95–65.94  15  \(\frac{15}{100}\) = 0.15  0.08 + 0.15 = 0.23 
65.95–67.94  40  \(\frac{40}{100}\) = 0.40  0.23 + 0.40 = 0.63 
67.95–69.94  17  \(\frac{17}{100}\) = 0.17  0.63 + 0.17 = 0.80 
69.95–71.94  12  \(\frac{12}{100}\) = 0.12  0.80 + 0.12 = 0.92 
71.95–73.94  7  \(\frac{7}{100}\) = 0.07  0.92 + 0.07 = 0.99 
73.95–75.94  1  \(\frac{1}{100}\) = 0.01  0.99 + 0.01 = 1.00 
Total = 100  Total = 1.00 
Table \(\PageIndex{4}\) Frequency Table of Soccer Player Height
The data in this table have been grouped into the following intervals:
 59.95 to 61.94 inches
 61.95 to 63.94 inches
 63.95 to 65.94 inches
 65.95 to 67.94 inches
 67.95 to 69.94 inches
 69.95 to 71.94 inches
 71.95 to 73.94 inches
 73.95 to 75.94 inches
In this sample, there are five players whose heights fall within the interval 59.95–61.94 inches, three players whose heights fall within the interval 61.95–63.94 inches, 15 players whose heights fall within the interval 63.95–65.94 inches, 40 players whose heights fall within the interval 65.95–67.94 inches, 17 players whose heights fall within the interval 67.95–69.94 inches, 12 players whose heights fall within the interval 69.95–71.94, seven players whose heights fall within the interval 71.95–73.94, and one player whose heights fall within the interval 73.95–75.94. All heights fall between the endpoints of an interval and not at the endpoints.
Exercise \(\PageIndex{1}\)
From Table \(\PageIndex{4}\), find the percentage of heights that are less than 65.95 inches.
Example \(\PageIndex{1}\)
From Table \(\PageIndex{5}\), find the percentage of heights that fall between 61.95 and 65.95 inches.
 Answer

Add the relative frequencies in the second and third rows: \(0.03 + 0.15 = 0.18\) or 18%.
Example \(\PageIndex{2}\)
Use the heights of the 100 male semiprofessional soccer players in Table \(\PageIndex{4}\). Fill in the blanks and check your answers.
 The percentage of heights that are from 67.95 to 71.95 inches is: ____.
 The percentage of heights that are from 67.95 to 73.95 inches is: ____.
 The percentage of heights that are more than 65.95 inches is: ____.
 The number of players in the sample who are between 61.95 and 71.95 inches tall is: ____.
 What kind of data are the heights?
 Describe how you could gather this data (the heights) so that the data are characteristic of all male semiprofessional soccer players.
Remember, you count frequencies. To find the relative frequency, divide the frequency by the total number of data values. To find the cumulative relative frequency, add all of the previous relative frequencies to the relative frequency for the current row.
 Answer

 29%
 36%
 77%
 87
 quantitative continuous
 get rosters from each team and choose a simple random sample from each
Exercise \(\PageIndex{2}\)
Table \(\PageIndex{5}\) shows the amount, in inches, of annual rainfall in a sample of towns.
Rainfall (inches)  Frequency  Relative frequency  Cumulative relative frequency 

2.95–4.96  6  \(\frac{6}{50}\) = 0.12  0.12 
4.97–6.98  7  \(\frac{7}{50}\) = 0.14  0.12 + 0.14 = 0.26 
6.99–9.00  15  \(\frac{15}{50}\) = 0.30  0.26 + 0.30 = 0.56 
9.01–11.02  8  \(\frac{8}{50}\) = 0.16  0.56 + 0.16 = 0.72 
11.03–13.04  9  \(\frac{9}{50}\) = 0.18  0.72 + 0.18 = 0.90 
13.05–15.07  5  \(\frac{5}{50}\) = 0.10  0.90 + 0.10 = 1.00 
Total = 50  Total = 1.00 
From Table \(\PageIndex{5}\), find the percentage of rainfall that is less than 9.01 inches.
Exercise \(\PageIndex{3}\)
From Table \(\PageIndex{5}\), find the percentage of rainfall that is between 6.99 and 13.05 inches.
Exercise \(\PageIndex{4}\)
Table \(\PageIndex{5}\) represents the amount, in inches, of annual rainfall in a sample of towns. What fraction of towns surveyed get between 11.03 and 13.05 inches of rainfall each year?
Example \(\PageIndex{3}\)
Nineteen people were asked how many miles, to the nearest mile, they commute to work each day. The data are as follows: 2; 5; 7; 3; 2; 10; 18; 15; 20; 7; 10; 18; 5; 12; 13; 12; 4; 5; 10. Table \(\PageIndex{6}\) was produced:
Data  Frequency  Relative frequency  Cumulative relative frequency 

3  3  \(\frac{3}{19}\)  0.1579 
4  1  \(\frac{1}{19}\)  0.2105 
5  3  \(\frac{3}{19}\)  0.1579 
7  2  \(\frac{2}{19}\)  0.2632 
10  3  \(\frac{4}{19}\)  0.4737 
12  2  \(\frac{2}{19}\)  0.7895 
13  1  \(\frac{1}{19}\)  0.8421 
15  1  \(\frac{1}{19}\)  0.8948 
18  1  \(\frac{1}{19}\)  0.9474 
20  1  \(\frac{1}{19}\)  1.0000 
 Is the table correct? If it is not correct, what is wrong?
 True or False: Three percent of the people surveyed commute three miles. If the statement is not correct, what should it be? If the table is incorrect, make the corrections.
 What fraction of the people surveyed commute five or seven miles?
 What fraction of the people surveyed commute 12 miles or more? Less than 12 miles? Between five and 13 miles (not including five and 13 miles)?
 Answer

 No. The frequency column sums to 18, not 19. Not all cumulative relative frequencies are correct.
 False. The frequency for three miles should be one; for two miles (left out), two. The cumulative relative frequency column should read: 0.1052, 0.1579, 0.2105, 0.3684, 0.4737, 0.6316, 0.7368, 0.7895, 0.8421, 0.9474, 1.0000.
 \(\frac{5}{19}\)
 \(\frac{7}{19}, \frac{12}{19}, \frac{7}{19)\)
Example \(\PageIndex{4}\)
Table \(\PageIndex{7}\) contains the total number of deaths worldwide as a result of earthquakes for the period from 2000 to 2012.
Year  Total number of deaths 

2000  231 
2001  21,357 
2002  11,685 
2003  33,819 
2004  228,802 
2005  88,003 
2006  6,605 
2007  712 
2008  88,011 
2009  1,790 
2010  320,120 
2011  21,953 
2012  768 
Total  823,856 
Answer the following questions.
 What is the frequency of deaths measured from 2006 through 2009?
 What percentage of deaths occurred after 2009?
 What is the relative frequency of deaths that occurred in 2003 or earlier?
 What is the percentage of deaths that occurred in 2004?
 What kind of data are the numbers of deaths?
 The Richter scale is used to quantify the energy produced by an earthquake. Examples of Richter scale numbers are 2.3, 4.0, 6.1, and 7.0. What kind of data are these numbers?
 Answer

 97,118 (11.8%)
 41.6%
 67,092/823,356 or 0.081 or 8.1 %
 27.8%
 Quantitative discrete
 Quantitative continuous
Exercise \(\PageIndex{5}\)
Table \(\PageIndex{8}\) contains the total number of fatal motor vehicle traffic crashes in the United States for the period from 1994 to 2011.
Year  Total number of crashes  Year  Total number of crashes 

1994  36,254  2004  38,444 
1995  37,241  2005  39,252 
1996  37,494  2006  38,648 
1997  37,324  2007  37,435 
1998  37,107  2008  34,172 
1999  37,140  2009  30,862 
2000  37,526  2010  30,296 
2001  37,862  2011  29,757 
2002  38,491  Total  653,782 
2003  38,477 
Answer the following questions.
 What is the frequency of deaths measured from 2000 through 2004?
 What percentage of deaths occurred after 2006?
 What is the relative frequency of deaths that occurred in 2000 or before?
 What is the percentage of deaths that occurred in 2011?
 What is the cumulative relative frequency for 2006? Explain what this number tells you about the data.
StemandLeaf Graphs (Stemplots), Line Graphs, and Bar Graphs
One simple graph, the stemandleaf graph or stemplot, comes from the field of exploratory data analysis. It is a good choice when the data sets are small. To create the plot, divide each observation of data into a stem and a leaf. The leaf consists of a final significant digit. For example, 23 has stem 2 and leaf 3. The number 432 has stem 43 and leaf 2. Likewise, the number 5,432 has stem 543 and leaf 2. The decimal 9.3 has stem 9 and leaf 3. Write the stems in a vertical line from smallest to largest. Draw a vertical line to the right of the stems. Then write the leaves in increasing order next to their corresponding stem.
Example \(\PageIndex{5}\)
For Susan Dean's spring precalculus class, scores for the first exam were as follows (smallest to largest):
33; 42; 49; 49; 53; 55; 55; 61; 63; 67; 68; 68; 69; 69; 72; 73; 74; 78; 80; 83; 88; 88; 88; 90; 92; 94; 94; 94; 94; 96; 100
Stem  Leaf 

3  3 
4  2 9 9 
5  3 5 5 
6  1 3 7 8 8 9 9 
7  2 3 4 8 
8  0 3 8 8 8 
9  0 2 4 4 4 4 6 
10  0 
The stemplot shows that most scores fell in the 60s, 70s, 80s, and 90s. Eight out of the 31 scores or approximately 26% (8/31) were in the 90s or 100, a fairly high number of As.
Exercise \(\PageIndex{6}\)
For the Park City basketball team, scores for the last 30 games were as follows (smallest to largest):
32; 32; 33; 34; 38; 40; 42; 42; 43; 44; 46; 47; 47; 48; 48; 48; 49; 50; 50; 51; 52; 52; 52; 53; 54; 56; 57; 57; 60; 61
Construct a stem plot for the data.
The stemplot is a quick way to graph data and gives an exact picture of the data. You want to look for an overall pattern and any outliers. An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500) while others may indicate that something unusual is happening. It takes some background information to explain outliers, so we will cover them in more detail later.
Example \(\PageIndex{6}\)
The data are the distances (in kilometers) from a home to local supermarkets. Create a stemplot using the data:
1.1; 1.5; 2.3; 2.5; 2.7; 3.2; 3.3; 3.3; 3.5; 3.8; 4.0; 4.2; 4.5; 4.5; 4.7; 4.8; 5.5; 5.6; 6.5; 6.7; 12.3
Do the data seem to have any concentration of values?
NOTE
The leaves are to the right of the decimal.
 Answer

The value 12.3 may be an outlier. Values appear to concentrate at 3 and 4 kilometers.
Stem Leaf 1 1 5 2 3 5 7 3 2 3 3 5 8 4 0 2 5 5 7 8 5 5 6 6 5 7 7 8 9 10 11 12 3 Table \(\PageIndex{10}\)
Exercise \(\PageIndex{7}\)
The following data show the distances (in miles) from the homes of offcampus statistics students to the college. Create a stem plot using the data and identify any outliers:
0.5; 0.7; 1.1; 1.2; 1.2; 1.3; 1.3; 1.5; 1.5; 1.7; 1.7; 1.8; 1.9; 2.0; 2.2; 2.5; 2.6; 2.8; 2.8; 2.8; 3.5; 3.8; 4.4; 4.8; 4.9; 5.2; 5.5; 5.7; 5.8; 8.0
Example \(\PageIndex{7}\)
A sidebyside stemandleaf plot allows a comparison of the two data sets in two columns. In a sidebyside stemandleaf plot, two sets of leaves share the same stem. The leaves are to the left and the right of the stems. Table \(\PageIndex{11}\) and Table \(\PageIndex{12}\) show the ages of presidents at their inauguration and at their death. Construct a sidebyside stemandleaf plot using this data.
 Answer

Ages at Inauguration Ages at Death 9 9 8 7 7 7 6 3 2 4 6 9 8 7 7 7 7 6 6 6 5 5 5 5 4 4 4 4 4 2 2 1 1 1 1 1 0 5 3 6 6 7 7 8 9 8 5 4 4 2 1 1 1 0 6 0 0 3 3 4 4 5 6 7 7 7 8 7 0 0 1 1 1 4 7 8 8 9 8 0 1 3 5 8 9 0 0 3 3 Table \(\PageIndex{13}\)
President  Age  President  Age  President  Age 

Washington  57  Lincoln  52  Hoover  54 
J. Adams  61  A. Johnson  56  F. Roosevelt  51 
Jefferson  57  Grant  46  Truman  60 
Madison  57  Hayes  54  Eisenhower  62 
Monroe  58  Garfield  49  Kennedy  43 
J. Q. Adams  57  Arthur  51  L. Johnson  55 
Jackson  61  Cleveland  47  Nixon  56 
Van Buren  54  B. Harrison  55  Ford  61 
W. H. Harrison  68  Cleveland  55  Carter  52 
Tyler  51  McKinley  54  Reagan  69 
Polk  49  T. Roosevelt  42  G.H.W. Bush  64 
Taylor  64  Taft  51  Clinton  47 
Fillmore  50  Wilson  56  G. W. Bush  54 
Pierce  48  Harding  55  Obama  47 
Buchanan  65  Coolidge  51  Trump  70 
President  Age  President  Age  President  Age 

Washington  67  Lincoln  56  Hoover  90 
J. Adams  90  A. Johnson  66  F. Roosevelt  63 
Jefferson  83  Grant  63  Truman  88 
Madison  85  Hayes  70  Eisenhower  78 
Monroe  73  Garfield  49  Kennedy  46 
J. Q. Adams  80  Arthur  56  L. Johnson  64 
Jackson  78  Cleveland  71  Nixon  81 
Van Buren  79  B. Harrison  67  Ford  93 
W. H. Harrison  68  Cleveland  71  Reagan  93 
Tyler  71  McKinley  58  
Polk  53  T. Roosevelt  60  
Taylor  65  Taft  72  
Fillmore  74  Wilson  67  
Pierce  64  Harding  57  
Buchanan  77  Coolidge  60 
Another type of graph that is useful for specific data values is a line graph. In the particular line graph shown in Example \(\PageIndex{8}\), the xaxis(horizontal axis) consists of data values and the yaxis (vertical axis) consists of frequency points. The frequency points are connected using line segments.
Example \(\PageIndex{8}\)
In a survey, 40 mothers were asked how many times per week a teenager must be reminded to do his or her chores. The results are shown in Table \(\PageIndex{14}\) and in Figure \(\PageIndex{1}\).
Number of times teenager is reminded  Frequency 

0  2 
1  5 
2  8 
3  14 
4  7 
5  4 
Bar graphs consist of bars that are separated from each other. The bars can be rectangles or they can be rectangular boxes (used in threedimensional plots), and they can be vertical or horizontal. The bar graph shown in Example \(\PageIndex{9}\) has age groups represented on the xaxis and proportions on the yaxis.
Example \(\PageIndex{9}\)
By the end of 2011, Facebook had over 146 million users in the United States. Table \(\PageIndex{16}\) shows three age groups, the number of users in each age group, and the proportion (%) of users in each age group. Construct a bar graph using this data.
Age groups  Number of Facebook users  Proportion (%) of Facebook users 

13–25  65,082,280  45% 
26–44  53,300,200  36% 
45–64  27,885,100  19% 
Exercise \(\PageIndex{9}\)
The population in Park City is made up of children, workingage adults, and retirees. Table \(\PageIndex{17}\) shows the three age groups, the number of people in the town from each age group, and the proportion (%) of people in each age group. Construct a bar graph showing the proportions.
Age groups  Number of people  Proportion of population 

Children  67,059  19% 
Workingage adults  152,198  43% 
Retirees  131,662  38% 
Example \(\PageIndex{10}\)
The columns in Table \(\PageIndex{18}\) contain: the race or ethnicity of students in U.S. Public Schools for the class of 2011, percentages for the Advanced Placement examine population for that class, and percentages for the overall student population. Create a bar graph with the student race or ethnicity (qualitative data) on the xaxis, and the Advanced Placement examinee population percentages on the yaxis.
Race/ethnicity  AP examinee population  Overall student population 

1 = Asian, Asian American or Pacific Islander  10.3%  5.7% 
2 = Black or African American  9.0%  14.7% 
3 = Hispanic or Latino  17.0%  17.6% 
4 = American Indian or Alaska Native  0.6%  1.1% 
5 = White  57.1%  59.2% 
6 = Not reported/other  6.0%  1.7% 
Exercise \(\PageIndex{10}\)

Park city is broken down into six voting districts. Table \(\PageIndex{19}\) shows the percent of the total registered voter population that lives in each district as well as the percent total of the entire population that lives in each district. Construct a bar graph that shows the registered voter population by district.
District Registered voter population Overall city population 1 15.5% 19.4% 2 12.2% 15.6% 3 9.8% 9.0% 4 17.4% 18.5% 5 22.8% 20.7% 6 22.3% 16.8% Table \(\PageIndex{19}\)
Example \(\PageIndex{11}\)
Below is a twoway table showing the types of pets owned by men and women:
Dogs  Cats  Fish  Total  

Men  4  2  2  8 
Women  4  6  2  12 
Total  8  8  4  20 
Given these data, calculate the conditional distributions for the subpopulation of men who own each pet type.
 Answer

 Men who own dogs = 4/8 = 0.5
 Men who own cats = 2/8 = 0.25
 Men who own fish = 2/8 = 0.25
Note: The sum of all of the conditional distributions must equal one. In this case, 0.5 + 0.25 + 0.25 = 1; therefore, the solution "checks".
Qualitative Data Discussion
Below are tables comparing the number of parttime and fulltime students at De Anza College and Foothill College enrolled for the spring 2010 term. The tables display counts (frequencies) and percentages or proportions (relative frequencies). The percent columns make comparing the same categories in the colleges easier. Displaying percentages along with the numbers is often helpful, but it is particularly important when comparing sets of data that do not have the same totals, such as the total enrollments for both colleges in this example. Notice how much larger the percentage for parttime students at Foothill College is compared to De Anza College.
De Anza College  Foothill College  

Number  Percent  Number  Percent  
Fulltime  9,200  40.9%  Fulltime  4,059  28.6%  
Parttime  13,296  59.1%  Parttime  10,124  71.4%  
Total  22,496  100%  Total  14,183  100% 
Tables are a good way of organizing and displaying data. But graphs can be even more helpful in understanding the data. There are no strict rules concerning which graphs to use. Two graphs that are used to display qualitative(categorical) data are pie charts and bar graphs.
 In a pie chart, categories of data are represented by wedges in a circle and are proportional in size to the percent of individuals in each category.
 In a bar graph, the length of the bar for each category is proportional to the number or percent of individuals in each category. Bars may be vertical or horizontal.
 A Pareto chart consists of bars that are sorted into order by category size (largest to smallest).
Look at Figures \(\PageIndex{4}\) and \(\PageIndex{5}\) and determine which graph (pie or bar) you think displays the comparisons better.
It is a good idea to look at a variety of graphs to see which is the most helpful in displaying the data. We might make different choices of what we think is the “best” graph depending on the data and the context. Our choice also depends on what we are using the data for.
Percentages That Add to More (or Less) Than 100%
Sometimes percentages add up to be more than 100% (or less than 100%). In the graph, the percentages add to more than 100% because students can be in more than one category. A bar graph is appropriate to compare the relative size of the categories. A pie chart cannot be used. It also could not be used if the percentages added to less than 100%.
Characteristic/category  Percent 

Fulltime students  40.9% 
Students who intend to transfer to a 4year educational institution  48.6% 
Students under age 25  61.0% 
TOTAL  150.5% 
Omitting Categories/Missing Data
The table displays Ethnicity of Students but is missing the "Other/Unknown" category. This category contains people who did not feel they fit into any of the ethnicity categories or declined to respond. Notice that the frequencies do not add up to the total number of students. In this situation, create a bar graph and not a pie chart.
Frequency  Percent  

Asian  8,794  36.1% 
Black  1,412  5.8% 
Filipino  1,298  5.3% 
Hispanic  4,180  17.1% 
Native American  146  0.6% 
Pacific Islander  236  1.0% 
White  5,978  24.5% 
TOTAL  22,044 out of 24,382  90.4% out of 100% 
The following graph is the same as the previous graph but the “Other/Unknown” percent (9.6%) has been included. The “Other/Unknown” category is large compared to some of the other categories (Native American, 0.6%, Pacific Islander 1.0%). This is important to know when we think about what the data are telling us.
This particular bar graph in Figure \(\PageIndex{9}\) is a Pareto chart. The Pareto chart has the bars sorted from largest to smallest and is easier to read and interpret.
Pie Charts: No Missing Data
The following pie charts have the “Other/Unknown” category included (since the percentages must add to 100%). The chart in Figure \(\PageIndex{10}\).
Histograms, Frequency Polygons, and Time Series Graphs
For most of the work you do in this book, you will use a histogram to display the data. One advantage of a histogram is that it can readily display large data sets. A rule of thumb is to use a histogram when the data set consists of 100 values or more.
A histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents (for instance, distance from your home to school). The vertical axis is labeled either frequency or relative frequency (or percent frequency or probability). The graph will have the same shape with either label. The histogram (like the stemplot) can give you the shape of the data, the center, and the spread of the data.
The relative frequency is equal to the frequency for an observed value of the data divided by the total number of data values in the sample. (Remember, frequency is defined as the number of times a value occurs.) If:
 \(f\) = frequency
 \(n\) = total number of data values (or the sum of the individual frequencies), and
 \(RF\) = relative frequency,
then:
\[\RF=\frac{f}{n}\nonumber]
For example, if three students in Mr. Ahab's English class of 40 students received from 90% to 100%, then, \(f = 3\), \(n = 40\), and \(RF = \frac{f}{n} = \frac{3}{40} = 0.075\). In other words, 7.5% of the students received 90–100%, and 90–100% are quantitative measures.
To construct a histogram, first decide how many bars or intervals, also called classes, represent the data. Many histograms consist of five to 15 bars or classes for clarity. The number of bars needs to be chosen. Choose a starting point for the first interval to be less than the smallest data value. A convenient starting point is a lower value carried out to one more decimal place than the value with the most decimal places. For example, if the value with the most decimal places is 6.1 and this is the smallest value, a convenient starting point is 6.05 (6.1 – 0.05 = 6.05). We say that 6.05 has more precision. If the value with the most decimal places is 2.23 and the lowest value is 1.5, a convenient starting point is 1.495 (1.5 – 0.005 = 1.495). If the value with the most decimal places is 3.234 and the lowest value is 1.0, a convenient starting point is 0.9995 (1.0 – 0.0005 = 0.9995). If all the data happen to be integers and the smallest value is 2, then a convenient starting point is 1.5 (2 – 0.5 = 1.5). Also, when the starting point and other boundaries are carried to one additional decimal place, no data value will fall on a boundary. The next two examples go into detail about how to construct a histogram using continuous data and how to create a histogram using discrete data.
Example \(\PageIndex{12}\)
The following data are the heights (in inches to the nearest half inch) of 100 male semiprofessional soccer players. The heights are continuous data, since height is measured.
60; 60.5; 61; 61; 61.5
63.5; 63.5; 63.5
64; 64; 64; 64; 64; 64; 64; 64.5; 64.5; 64.5; 64.5; 64.5; 64.5; 64.5; 64.5
66; 66; 66; 66; 66; 66; 66; 66; 66; 66; 66.5; 66.5; 66.5; 66.5; 66.5; 66.5; 66.5; 66.5; 66.5; 66.5; 66.5; 67; 67; 67; 67; 67; 67; 67; 67; 67; 67; 67; 67; 67.5; 67.5; 67.5; 67.5; 67.5; 67.5; 67.5
68; 68; 69; 69; 69; 69; 69; 69; 69; 69; 69; 69; 69.5; 69.5; 69.5; 69.5; 69.5
70; 70; 70; 70; 70; 70; 70.5; 70.5; 70.5; 71; 71; 71
72; 72; 72; 72.5; 72.5; 73; 73.5
74
The smallest data value is 60. Since the data with the most decimal places has one decimal (for instance, 61.5), we want our starting point to have two decimal places. Since the numbers 0.5, 0.05, 0.005, etc. are convenient numbers, use 0.05 and subtract it from 60, the smallest value, for the convenient starting point.
60 – 0.05 = 59.95 which is more precise than, say, 61.5 by one decimal place. The starting point is, then, 59.95.
The largest value is 74, so 74 + 0.05 = 74.05 is the ending value.
Next, calculate the width of each bar or class interval. To calculate this width, subtract the starting point from the ending value and divide by the number of bars (you must choose the number of bars you desire). Suppose you choose eight bars.
\[\frac{74.05−59.95}{8}=1.76\non\nonumber\]
NOTE
We will round up to two and make each bar or class interval two units wide. Rounding up to two is one way to prevent a value from falling on a boundary. Rounding to the next number is often necessary even if it goes against the standard rules of rounding. For this example, using 1.76 as the width would also work. A guideline that is followed by some for the width of a bar or class interval is to take the square root of the number of data values and then round to the nearest whole number, if necessary. For example, if there are 150 values of data, take the square root of 150 and round to 12 bars or intervals.
The boundaries are:
 59.95
 59.95 + 2 = 61.95
 61.95 + 2 = 63.95
 63.95 + 2 = 65.95
 65.95 + 2 = 67.95
 67.95 + 2 = 69.95
 69.95 + 2 = 71.95
 71.95 + 2 = 73.95
 73.95 + 2 = 75.95
The heights 60 through 61.5 inches are in the interval 59.95–61.94. The heights that are 63.5 are in the interval 61.95–63.94. The heights that are 64 through 64.5 are in the interval 63.95–65.94. The heights 66 through 67.5 are in the interval 65.95–67.94. The heights 68 through 69.5 are in the interval 67.95–69.94. The heights 70 through 71 are in the interval 69.95–71.94. The heights 72 through 73.5 are in the interval 71.95–73.94. The height 74 is in the interval 73.95–75.94.
The following histogram displays the heights on the xaxis and relative frequency on the yaxis.
Example \(\PageIndex{13}\)
Create a histogram for the following data: the number of books bought by 50 parttime college students at ABC College. The number of books is discrete data, since books are counted.
1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1
2; 2; 2; 2; 2; 2; 2; 2; 2; 2
3; 3; 3; 3; 3; 3; 3; 3; 3; 3; 3; 3; 3; 3; 3; 3
4; 4; 4; 4; 4; 4
5; 5; 5; 5; 5
6; 6
Eleven students buy 1 book. Ten students buy 2 books. Sixteen students buy 3 books. Six students buy 4 books. Five students buy 5 books. Two students buy 6 books.
Because the data are integers, subtract 0.5 from 1, the smallest data value and add 0.5 to 6, the largest data value. Then the starting point is 0.5 and the ending value is 6.5.
Next, calculate the width of each bar or class interval. If the data are discrete and there are not too many different values, a width that places the data values in the middle of the bar or class interval is the most convenient. Since the data consist of the numbers 1, 2, 3, 4, 5, 6, and the starting point is 0.5, a width of one places the 1 in the middle of the interval from 0.5 to 1.5, the 2 in the middle of the interval from 1.5 to 2.5, the 3 in the middle of the interval from 2.5 to 3.5, the 4 in the middle of the interval from _______ to _______, the 5 in the middle of the interval from _______ to _______, and the _______ in the middle of the interval from _______ to _______ .
Solution
Calculate the number of bars as follows:
\[\frac{6.5−0.5}{\text{number of bars}}=1\nonumber\]
where 1 is the width of a bar. Therefore, bars = 6.
The following histogram displays the number of books on the xaxis and the frequency on the yaxis.
Example \(\PageIndex{14}\)
Using this data set, construct a histogram.
Number of hours my classmates spent playing video games on weekends  

9.95  10  2.25  16.75  0 
19.5  22.5  7.5  15  12.75 
5.5  11  10  20.75  17.5 
23  21.9  24  23.75  18 
20  15  22.9  18.8  20.5 
 Answer

Figure \(\PageIndex{13}\) Some values in this data set fall on boundaries for the class intervals. A value is counted in a class interval if it falls on the left boundary, but not if it falls on the right boundary. Different researchers may set up histograms for the same data in different ways. There is more than one correct way to set up a histogram.
Frequency Polygons
Frequency polygons are analogous to line graphs, and just as line graphs make continuous data visually easy to interpret, so too do frequency polygons.
To construct a frequency polygon, first examine the data and decide on the number of intervals, or class intervals, to use on the xaxis and yaxis. After choosing the appropriate ranges, begin plotting the data points. After all the points are plotted, draw line segments to connect them.
Example \(\PageIndex{15}\)
A frequency polygon was constructed from the frequency table below.
Lower bound  Upper bound  Frequency  Cumulative frequency 

49.5  59.5  5  5 
59.5  69.5  10  15 
69.5  79.5  30  45 
79.5  89.5  40  85 
89.5  99.5  15  100 
The first label on the xaxis is 44.5. This represents an interval extending from 39.5 to 49.5. Since the lowest test score is 54.5, this interval is used only to allow the graph to touch the xaxis. The point labeled 54.5 represents the next interval, or the first “real” interval from the table, and contains five scores. This reasoning is followed for each of the remaining intervals with the point 104.5 representing the interval from 99.5 to 109.5. Again, this interval contains no data and is only used so that the graph will touch the xaxis. Looking at the graph, we say that this distribution is skewed because one side of the graph does not mirror the other side.
Frequency polygons are useful for comparing distributions. This is achieved by overlaying the frequency polygons drawn for different data sets.
Example \(\PageIndex{16}\)
We will construct an overlay frequency polygon comparing the scores from Example \(\PageIndex{15}\) with the students’ final numeric grade.
Lower bound  Upper bound  Frequency  Cumulative frequency 

49.5  59.5  5  5 
59.5  69.5  10  15 
69.5  79.5  30  45 
79.5  89.5  40  85 
89.5  99.5  15  100 
Lower bound  Upper bound  Frequency  Cumulative frequency 

49.5  59.5  10  10 
59.5  69.5  10  20 
69.5  79.5  30  50 
79.5  89.5  45  95 
89.5  99.5  5  100 
Constructing a Time Series Graph
Suppose that we want to study the temperature range of a region for an entire month. Every day at noon we note the temperature and write this down in a log. A variety of statistical studies could be done with these data. We could find the mean or the median temperature for the month. We could construct a histogram displaying the number of days that temperatures reach a certain range of values. However, all of these methods ignore a portion of the data that we have collected.
One feature of the data that we may want to consider is that of time. Since each date is paired with the temperature reading for the day, we do not have to think of the data as being random. We can instead use the times given to impose a chronological order on the data. A graph that recognizes this ordering and displays the changing temperature as the month progresses is called a time series graph.
To construct a time series graph, we must look at both pieces of our paired data set. We start with a standard Cartesian coordinate system. The horizontal axis is used to plot the date or time increments, and the vertical axis is used to plot the values of the variable that we are measuring. By doing this, we make each point on the graph correspond to a date and a measured quantity. The points on the graph are typically connected by straight lines in the order in which they occur.
Example \(\PageIndex{17}\)
The following data shows the Annual Consumer Price Index, each month, for ten years. Construct a time series graph for the Annual Consumer Price Index data only.
Year  Jan  Feb  Mar  Apr  May  Jun  Jul 

2003  181.7  183.1  184.2  183.8  183.5  183.7  183.9 
2004  185.2  186.2  187.4  188.0  189.1  189.7  189.4 
2005  190.7  191.8  193.3  194.6  194.4  194.5  195.4 
2006  198.3  198.7  199.8  201.5  202.5  202.9  203.5 
2007  202.416  203.499  205.352  206.686  207.949  208.352  208.299 
2008  211.080  211.693  213.528  214.823  216.632  218.815  219.964 
2009  211.143  212.193  212.709  213.240  213.856  215.693  215.351 
2010  216.687  216.741  217.631  218.009  218.178  217.965  218.011 
2011  220.223  221.309  223.467  224.906  225.964  225.722  225.922 
2012  226.665  227.663  229.392  230.085  229.815  229.478  229.104 
Year  Aug  Sep  Oct  Nov  Dec  Annual 

2003  184.6  185.2  185.0  184.5  184.3  184.0 
2004  189.5  189.9  190.9  191.0  190.3  188.9 
2005  196.4  198.8  199.2  197.6  196.8  195.3 
2006  203.9  202.9  201.8  201.5  201.8  201.6 
2007  207.917  208.490  208.936  210.177  210.036  207.342 
2008  219.086  218.783  216.573  212.425  210.228  215.303 
2009  215.834  215.969  216.177  216.330  215.949  214.537 
2010  218.312  218.439  218.711  218.803  219.179  218.056 
2011  226.545  226.889  226.421  226.230  225.672  224.939 
2012  230.379  231.407  231.317  230.221  229.601  229.594 
 Answer

Figure \(\PageIndex{9}\)
Exercise \(\PageIndex{18}\)
The following table is a portion of a data set from www.worldbank.org. Use the table to construct a time series graph for CO_{2}emissions for the United States.
Year  Ukraine  United Kingdom  United States 

2003  352,259  540,640  5,681,664 
2004  343,121  540,409  5,790,761 
2005  339,029  541,990  5,826,394 
2006  327,797  542,045  5,737,615 
2007  328,357  528,631  5,828,697 
2008  323,657  522,247  5,656,839 
2009  272,176  474,579  5,299,563 
Uses of a Time Series Graph
Time series graphs are important tools in various applications of statistics. When recording values of the same variable over an extended period of time, sometimes it is difficult to discern any trend or pattern. However, once the same data points are displayed graphically, some features jump out. Time series graphs make trends easy to spot.
How NOT to Lie with Statistics
It is important to remember that the very reason we develop a variety of methods to present data is to develop insights into the subject of what the observations represent. We want to get a "sense" of the data. Are the observations all very much alike or are they spread across a wide range of values, are they bunched at one end of the spectrum or are they distributed evenly and so on. We are trying to get a visual picture of the numerical data. Shortly we will develop formal mathematical measures of the data, but our visual graphical presentation can say much. It can, unfortunately, also say much that is distracting, confusing and simply wrong in terms of the impression the visual leaves. Many years ago Darrell Huff wrote the book How to Lie with Statistics. It has been through 25 plus printings and sold more than one and onehalf million copies. His perspective was a harsh one and used many actual examples that were designed to mislead. He wanted to make people aware of such deception, but perhaps more importantly to educate so that others do not make the same errors inadvertently.
Again, the goal is to enlighten with visuals that tell the story of the data. Pie charts have a number of common problems when used to convey the message of the data. Too many pieces of the pie overwhelm the reader. More than perhaps five or six categories ought to give an idea of the relative importance of each piece. This is after all the goal of a pie chart, what subset matters most relative to the others. If there are more components than this then perhaps an alternative approach would be better or perhaps some can be consolidated into an "other" category. Pie charts cannot show changes over time, although we see this attempted all too often. In federal, state, and city finance documents pie charts are often presented to show the components of revenue available to the governing body for appropriation: income tax, sales tax motor vehicle taxes and so on. In and of itself this is interesting information and can be nicely done with a pie chart. The error occurs when two years are set sidebyside. Because the total revenues change year to year, but the size of the pie is fixed, no real information is provided and the relative size of each piece of the pie cannot be meaningfully compared.
Histograms can be very helpful in understanding the data. Properly presented, they can be a quick visual way to present probabilities of different categories by the simple visual of comparing relative areas in each category. Here the error, purposeful or not, is to vary the width of the categories. This of course makes comparison to the other categories impossible. It does embellish the importance of the category with the expanded width because it has a greater area, inappropriately, and thus visually "says" that that category has a higher probability of occurrence.
Time series graphs perhaps are the most abused. A plot of some variable across time should never be presented on axes that change part way across the page either in the vertical or horizontal dimension. Perhaps the time frame is changed from years to months. Perhaps this is to save space or because monthly data was not available for early years. In either case this confounds the presentation and destroys any value of the graph. If this is not done to purposefully confuse the reader, then it certainly is either lazy or sloppy work.
Changing the units of measurement of the axis can smooth out a drop or accentuate one. If you want to show large changes, then measure the variable in small units, penny rather than thousands of dollars. And of course to continue the fraud, be sure that the axis does not begin at zero, zero. If it begins at zero, zero, then it becomes apparent that the axis has been manipulated.
Perhaps you have a client that is concerned with the volatility of the portfolio you manage. An easy way to present the data is to use long time periods on the time series graph. Use months or better, quarters rather than daily or weekly data. If that doesn't get the volatility down then spread the time axis relative to the rate of return or portfolio valuation axis. If you want to show "quick" dramatic growth, then shrink the time axis. Any positive growth will show visually "high" growth rates. Do note that if the growth is negative then this trick will show the portfolio is collapsing at a dramatic rate.
Again, the goal of descriptive statistics is to convey meaningful visuals that tell the story of the data. Purposeful manipulation is fraud and unethical at the worst, but even at its best, making these type of errors will lead to confusion on the part of the analysis.