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**Section I **

*Line Charts *

Charts are an efficient and practical way to convey information, especially for comparative purposes. Visual representations like line charts and control charts, for instance, are useful tools for making a point that could be quite difficult to put into words.

A line chart, also referred to as a line plot or line graph, is a chart that uses points connected by line segments from left to right track changes in value. The horizontal or x-axis shows a continuous progression of a value such as time while the vertical or y-axis shows the value for a particular metric of interest across the progression. In other words, a line chart shows the change in a quantity with respect to another (Provost & Murray, 2011).

Examples of line charts are shown below. The first line chart accessed from the Center for Disease Control and Prevention website shows the number of measles cases from 1950 to 1954. The independent variable, time, is shown on the x-axis while the dependent variable, the number of cases, is shown on the y-axis. The second line chart from the Agency for Healthcare Research and Quality shows the number of adult patients receiving hip joint replacement due to fracture with adverse events.

*Control Charts*

A control chart, like a line chart, is a graph used to show how a metric of interest changes. The main difference between a control chart and a line chart, however, is that the former has a central line that shows the average of the data, an upper line that shows the upper control limit, and a lower line that shows the lower control limit. Using these lines, one can determine whether the variation in a metric is stable or unpredictable (Dobi & Zempleni, 2019).

Examples of control charts are shown below. The first control chart shows the number of adverse drug events (ADEs) at a hospital dealing with the critically ill over a 1 year period. The independent variable, time, is shown on the x-axis while the dependent variable, the number of ADEs per 1000 doses, is shown in the y-axis. The second example of a control chart shows the infection rate of a disease over time.

**Section II**

*Scatter Plot Diagrams *

A scatter plot diagram, also known as a scatter graph or scatter chart, uses dots to show the values for different numeric variables. The position of each dot on the y-axis and x-axis indicates the values for a particular data point. This type of chart is used to determine the relationship between variables.

Scatter plots often show patterns or relationships. A positive correlation between variables occurs when the y variable increases as the x variable increases. Another type of correlation, negative correlation, occurs when the y variable increases as the x variable decreases. A no correlation situation occurs when there lacks a clear relationship between the y and the x variables (Provost & Murray, 2011).

Examples of scatter plot diagrams are shown below. The first scatter plot shows the relationship between the length of time of a consultation with a doctor in outpatient and patient satisfaction levels. The second example shows the Metro Health Index, a factor used to measure the obese or people who smoke, as it correlates with the median income in a city.

References

Dobi, B., & Zempléni, A. (2019). Markov chain‐based cost‐optimal control charts for health care data. *Quality and Reliability Engineering International*, *35*(5), 1379-1395.

Provost, L. P., & Murray, S. (2011). *The health care data guide: learning from data for improvement*. John Wiley & Sons.