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A line plot shows data on a number line with X marks above each value. It's useful for showing measurements, surveys, or counts. The pattern of X marks shows the distribution of data.
Create line plots from class measurements or surveys. Interpret existing line plots.
Misplacing marks; not aligning marks vertically; forgetting the number line scale.
You already know how to read and create basic line plots. Now the focus shifts to using line plots to analyze real data — measurements or survey results from your own world — and drawing meaningful conclusions from the pattern of marks. A line plot is not just a picture; it is a compressed record of every data point, arranged so that the *distribution* becomes visible at a glance.
The structure of a line plot connects directly to the number line you have worked with for years. The number line runs along the bottom, labeled with the possible data values. Each X mark above a value represents one data point — one observation, one measurement, one response. If five students measured a pencil as 7 centimeters, there will be 5 X's stacked above the 7. The height of the stack tells you frequency: how often that value appeared.
Reading a line plot means asking two kinds of questions. Frequency questions ask how many: "How many students got exactly 8 correct?" — count the X's above 8. Comparison questions ask about relationships: "Which score appeared most often?" — find the tallest stack. "Were there more students scoring above 10 or below 10?" — count and compare the X's on each side. The visual layout makes these comparisons much faster than scanning a list of numbers.
Creating a line plot from raw data is a two-step process. First, draw the number line with a range that covers all your data values — if your measurements go from 3 to 12, your number line should span at least that range. Second, place one X above the correct value for each data point, stacking them directly above each other so the column heights are easy to compare. The most common errors are misreading the scale (placing an X above the wrong number) and stacking marks unevenly so the columns look different heights than they really are. Neat, vertical alignment is not just aesthetic — it is what makes the plot readable.