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.
Histogram
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 two, 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.
Watch the following video for an example of how to draw a histogram.
Example 1The 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; 64.566; 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 Construct a relative frequency table and histogram. 
Show Answer

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. 
Example 2The following data are 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 one book. Ten students buy two books. Sixteen students buy three books. Six students buy four books. Five students buy five books. Two students buy six 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: [latex]\displaystyle\frac{{{6.5}{0.5}}}{{\text{number of bars}}}={1}[/latex] 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.

Create the histogram for Example 2 by using TICalculator:
 Press Y=. Press CLEAR to delete any equations.
 Press STAT 1:EDIT. If L1 has data in it, arrow up into the name L1, press CLEAR and then arrow down. If necessary, do the same for L2.
 Into L1, enter 1, 2, 3, 4, 5, 6.
 Into L2, enter 11, 10, 16, 6, 5, 2.
 Press WINDOW. Set Xmin = .5, Xscl = (6.5 – .5)/6, Ymin = –1, Ymax = 20, Yscl = 1, Xres = 1.
 Press 2nd Y=. Start by pressing 4:Plotsoff ENTER.
 Press 2nd Y=. Press 1:Plot1. Press ENTER. Arrow down to TYPE. Arrow to the 3rd picture (histogram). Press ENTER.
 Arrow down to Xlist: Enter L1 (2nd 1). Arrow down to Freq. Enter L2 (2nd 2).
 Press GRAPH.
 Use the TRACE key and the arrow keys to examine the histogram.
Example 3Using this data set, construct a histogram.



Practice Problems 1:The following data are the shoe sizes of 50 male students. The sizes are continuous data since shoe size is measured. Construct a histogram and calculate the width of each bar or class interval. Suppose you choose six bars. 9; 9; 9.5; 9.5; 10; 10; 10; 10; 10; 10; 10.5; 10.5; 10.5; 10.5; 10.5; 10.5; 10.5; 10.5 11; 11; 11; 11; 11; 11; 11; 11; 11; 11; 11; 11; 11; 11.5; 11.5; 11.5; 11.5; 11.5; 11.5; 11.5 12; 12; 12; 12; 12; 12; 12; 12.5; 12.5; 12.5; 12.5; 14 Option 1:
Option 2:

Practice Problem 2:The following data are the number of sports played by 50 student athletes. The number of sports is discrete data since sports are counted. 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1; 1 2; 2; 2; 2; 2; 2; 2; 2; 2; 2; 2; 2; 2; 2; 2; 2; 2; 2; 2; 2; 2; 2 3; 3; 3; 3; 3; 3; 3; 3 Solution20 student athletes play one sport. 22 student athletes play two sports. Eight student athletes play three sports. Fill in the blanks for the following sentence. Since the data consist of the numbers 1, 2, 3, and the starting point is 0.5, a width of one places the 1 in the middle of the interval 0.5 to _____, the 2 in the middle of the interval from _____ to _____, and the 3 in the middle of the interval from _____ to _____. Show Answer

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 4
A frequency polygon was constructed from the frequency table below.
Frequency Distribution for Calculus Final Test Scores  

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.
Example 5
We will construct an overlay frequency polygon comparing the scores with the students’ final numeric grade.
Frequency Distribution for Calculus Final Test Scores  

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 
Frequency Distribution for Calculus Final Grades  

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 
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 this 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.
Practice Problem 3:Construct a frequency polygon of U.S. Presidents’ ages at inauguration shown in the table.
Show Answer
Frequency polygons are useful for comparing distributions. This is achieved by overlaying the frequency polygons drawn for different data sets. 
Constructing a Time Series Graph
Example 6
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 
Try It
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.
CO2 Emissions  

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.
References
Data on annual homicides in Detroit, 1961–73, from Gunst & Mason’s book ‘Regression Analysis and its Application’, Marcel Dekker
“Timeline: Guide to the U.S. Presidents: Information on every president’s birthplace, political party, term of office, and more.” Scholastic, 2013. Available online at http://www.scholastic.com/teachers/article/timelineguideuspresidents (accessed April 3, 2013).
“Presidents.” Fact Monster. Pearson Education, 2007. Available online at http://www.factmonster.com/ipka/A0194030.html (accessed April 3, 2013).
“Food Security Statistics.” Food and Agriculture Organization of the United Nations. Available online at http://www.fao.org/economic/ess/essfs/en/ (accessed April 3, 2013).
“Consumer Price Index.” United States Department of Labor: Bureau of Labor Statistics. Available online at http://data.bls.gov/pdq/SurveyOutputServlet (accessed April 3, 2013).
“CO2 emissions (kt).” The World Bank, 2013. Available online at http://databank.worldbank.org/data/home.aspx (accessed April 3, 2013).
“Births Time Series Data.” General Register Office For Scotland, 2013. Available online at http://www.groscotland.gov.uk/statistics/theme/vitalevents/births/timeseries.html (accessed April 3, 2013).
“Demographics: Children under the age of 5 years underweight.” Indexmundi. Available online at http://www.indexmundi.com/g/r.aspx?t=50&v=2224&aml=en (accessed April 3, 2013).
Gunst, Richard, Robert Mason. Regression Analysis and Its Application: A DataOriented Approach. CRC Press: 1980.
“Overweight and Obesity: Adult Obesity Facts.” Centers for Disease Control and Prevention. Available online at http://www.cdc.gov/obesity/data/adult.html (accessed September 13, 2013).
Concept Review
A histogram is a graphic version of a frequency distribution. The graph consists of bars of equal width drawn adjacent to each other. The horizontal scale represents classes of quantitative data values and the vertical scale represents frequencies. The heights of the bars correspond to frequency values. Histograms are typically used for large, continuous, quantitative data sets. A frequency polygon can also be used when graphing large data sets with data points that repeat. The data usually goes on yaxis with the frequency being graphed on the xaxis. Time series graphs can be helpful when looking at large amounts of data for one variable over a period of time.