CK-12 Probability and Statistics - Advanced

2.2. Common Graphs and Data Plots http://www.ck12.org

TABLE2.21:

Year Municipal Waste Generated (Millions of Tons)

1990 269

1991 294

1992 281

1993 292

1994 307

1995 323

1996 327

1997 327

1998 340

Figure:Total Municipal Waste Generated in the US by Year in Millions of Tons. Source:http://www.zerowaste
america.org/MunicipalWasteManagementReport1998.htm

In this example, the time in years is the explanatory variable and the amount of municipal waste is the response
variable. It is not the passage of time thatcausesour waste to increase. Other factors such as population growth,
economic conditions, and societal habits and attitudes contribute as causes. But it would not make sense to view the
relationship between time and municipal waste in the opposite direction.

When one of the variables is time, it will almost always be the explanatory variable. Because time is a continuous
variable and we are very often interested in the change a variable exhibits over a period of time, there is some
meaning to the connection between the points in a plot involving time as an explanatory variable. In this case we
use aline plot.A line plot is simply a scatterplot in which we connect successive chronological observations with a
line segment to give more information about how the data is changing over a period of time. Here is the line plot for
the US Municipal Waste data:

It is easy to see general trends from this type of plot. For example, we can spot the year in which the most dramatic
increase occurred by looking at the steepest line (1990). We can also spot the years in which the waste output
decreased and/or remained about the same (1991 and 1996). It would be interesting to investigate some possible
reasons for the behaviors of these individual years.