Analyzing Graphical Data

Learning outcome

  • Compare and contrast graphical data to decipher information and make decisions

We looked at graph trends earlier to determine if there was a general statement that could be made about the data presented. But if you are a retail professional and are given accounting information or other data on a graph, you’ll also need to be able to make decisions based on what you can observe and infer.

Let’s take a look at a familiar graph of the Dow Jones Industrial Average.


What can we infer from this graph?
A graph of the DOW Jones Industrial Average from 1900 to 2000. The horizontal axis is measured in years, and the vertical axis measures the average in power ten increments, from 10 to 1000 to 10,000. In 1900, the average measures at around 80. Then, by 1920, the average has increased to about 100. From 1920 to 1930, the average rises to 400, however, it suddenly falls around the year 1930, and by 1940 the average is about 150. From 1940 to 1960, the average reaches approximately 600, then 800 by 1980. By the year 2000, The average has risen to about 10,000.

We already determined that the trend was an increase in value over time. We can also say that even after a sharp decrease, the value has risen back to the highest prior point within [latex]25[/latex] years. If you were asked the question, “Should I trust that the Dow Jones will continue to increase over time?”, your reply should be, “Based on historical data, the Dow Jones will continue to increase indefinitely.” There’s nothing on this graph that should lead us to believe otherwise.


The graph below shows the average monthly visitor count at White Sands National Monument sorted by month of the year. Next year, you plan to set up a roadside stand nearby to sell carved wooden souvenirs that you make in your garage. To make a decent profit, you need to dedicate half the year to making the wooden carvings and half the year to selling them. Based on the data below, which half of the year should you set up your roadside stand nearby to the park?

A graph of the average monthly visitation at White Sands National Monument from 1988 to 2016. Along the horizontal axis is each of the twelve months, and along the vertical is the total number of visits. The number of visits in January is 24,785. February is 28,731. March is 61,018. April is 53,040. May is 54,102. June is 52,308. July is 63,519. August is 50,948. September is 42,079. October is 36,994. November is 29,037. December is 27,961.

Now let’s take a look again at the graph of the marriage rates in Great Britain.


Let’s first examine the time frame [latex]1930[/latex] to [latex]1975[/latex]. During that time, the data on this graph is all over the place, but there is a slight trend of an increase in marriage rates. However, in the middle of this trend, there is a very sharp incline and then a very sharp decline. Why do you think this is?
A graph of the marriage rates in Great Britain from 1930 to 2010. The graph illustrates both male and female trends. The date range of World War 1, from 1939 to 1945, is labeled on the graph, as is the year of the Asylum and Immigration act, which is 2004. Starting in 1930, the male marriage rate was about 55, and the female was slightly above 40. By 1940, the male rate had risen to 80, and the female rate was above 60. A peak occured around this time. The next 3 or 4 years during the war saw a steady decline in marriage rates down to 55 and 40, until rates rose again in the last year of the war past 70 and 50. After another 5 years, the male rate was a little below 70 and the female was a little above 50. By 1960, those numbers hadn't changed much. However, by 1970 the male rate was about 75, and the female rate was around 60. By 1980, rates declined to 60 and below 50, respectively. Another 10 years and they'd be down to 40 and about 35. Come 2000, the rates were below 30. In 2004 when the Asylum and Immigration Act was passed, the trend of declining rates did not seem to change. And they continued to drop further into the 2000s, with a male rate slightly above 20 and a female rate around 20 in 2010.

On this graph they have highlighted the [latex]1930[/latex] to [latex]1945[/latex] time period as World War II. Often historical data is indicative of economic and political events. The strong peak at the start of this time period is evidence that prior to young men heading off to war, they were quickly marrying before deployment. The sharp valley indicates that a huge number of men were overseas and marriage was not a top priority as citizens were focused on supporting the military and struggling economy.

Try It

The following bar graph shows the number of customers helped per hour by each of the employees at Sofa Central, a furniture store downtown. Lara, the store manager, is trying to decide who she should promote to floor lead to help mentor the other employees on good sales and service practices. Who should Lara promote?
A sample bar chart. The horizontal axis has the months January, February, March, and April. TThe vertical axis runs from 0 to 20 and represents how many customers each employee helped that month. A legend distinguishes between 4 trends: Dan, Sophia, Jody, and Christina. In January, Dan measured at 4, Sophia at 8, Jody at 3, and Christina at 6. In February, Dan measured at 10, Sophia at 12, Jody at 6, and Christina at 12. In March, Dan measured at 8, Sophia at 10, Jody at 6, and Christina at 7. In April, Dan measured at 6, Sophia at 9, Jody at 5, and Christina at 10.


Kelsey would like to provide a raffle prize that appeals the most people based on their main transportation choice. The pie chart below shows the poll results from the employee newsletter where people were asked how they most commonly get to work. Should the raffle prize be the best parking spot in the lot for a month, a new bike lock and free tune-up at the bicycle shop, a gift card for the local taxi company, a month-long public transit pass, or a new motorcycle helmet?

A pie chart for traffic. Cars account for 43% of traffic, while taxis account for 17%, and both buses and motorcycles account for 13% each. Next is trucks at 8%, followed by 3 wheels at 2%. Both wheelbarrows and bicycles account for 1% each.

Graphs allow us to visualize data to quickly see trends or odd outliers to better make decisions. Rather than basing decisions off your gut feeling about something, having data to support that decision is key. Data displayed in graphs gives you the chance to “see” where those values are coming from, use your knowledge to explain variations and results, and anticipate where the data is headed.


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