Graphing and Describing Data in Everyday Life

Graphing and Describing Data in Everyday Life

Graphing and Describing Data in Everyday Life 150 150 Peter

Graphing and Describing Data in Everyday Life

Suppose that you have two sets of data to work with. The first set is a list of all the injuries that were seen in a clinic in a month’s time. The second set contains data on the number of minutes that each patient spent in the waiting room of a doctor’s office. You can make assumptions about other information or variables that are included in each data set.

For each data set, propose your idea of how best to represent the key information. To organize your data would you choose to use a frequency table, a cumulative frequency table, or a relative frequency table? Why?

What type of graph would you use to display the organized data from each frequency distribution? What would be shown on each of the axes for each graph?

Sample Paper

Graphing and Describing Data in Everyday Life

The two data sets require articulate presentation methods to enhance information delivery. For the first data set, a frequency table would be sufficient to organize the data. One would create categories showing the body parts that had the injury. The four main categories would be head, legs, arms, and torso. Thus, this grouping would facilitate effective data representation. After generating the frequency table, one can use a pie chart to display the data. The pie chart would be ideal since there are a few categories (Edrawsoft, 2022). Therefore, it allows the audience to identify the more problematic areas quickly. A pie chart does not have conventional axes, but it would be necessary to use distinct colors or patterns to different the four sectors. An accompanying key and a category title within the sectors would enhance information delivery.

The second data set would require additional information on the shift during which each patient seeks treatment for their injury. Assuming the facility has three 8-hour shifts per day, one would group the data using a cumulative frequency table. The total number of waiting minutes per shift would appear at the final entry at the end of the month. Thus, it would be easy to determine which shift has the longest delays. A compound bar graph would be ideal in displaying the data since it allows multi-variate comparison (Holmes et al., 2017). The x-axis would show the day-t-day progression, with each day having three bars, one for each shift. The y-axis would show the number of waiting minutes. The compound bar graph enhances the ability to spot variations in the trend (Kodag, 2021), enhancing visual appeal and communication efficacy.


Edrawsoft. (2022). Pie Chart.

Holmes, A., Illowsky, B., & Dean, S. (2017). Introductory Business Statistics. OpenStax.

Kodag, N. (2021, Jul. 13). Compound Bar Graph.