Final answer:
A time series graph would be the ideal choice for Dr. Kennedy to visualize the clinic's monthly medicine orders and predict future trends. This type of graph can compress nearly 600 data points into an easily interpretable format, highlighting patterns and trends over time.
Step-by-step explanation:
To assist Dr. Kennedy in predicting how much medicine their clinic will order in the following months based on historical ordering data, a time series graph would be the most suitable graphical representation. A time series graph can effectively compress lots of data and show trends over time which is essential for making predictions.
Monthly data since 1960 constitutes nearly 600 data points, which can be represented in a way that is much easier to interpret than a numerical list.
A time series graph would show the amount of medicine ordered on the vertical axis and time (in months) on the horizontal axis. By plotting these data points, Dr. Kennedy can observe patterns, such as seasonal increases or decreases in medicine orders, and use this to forecast future ordering needs.
Since the data is chronological, it provides a clear visual representation of trends and potential cyclical behavior in the clinic's medicine ordering habits.