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A line plot displays data on a number line using Xs or dots above each value. It's useful for measurement data (lengths, heights) and shows the distribution of values, with gaps and clusters visible.
You already know what a line plot is: a number line with Xs (or dots) stacked above it to show how many times each value appears in a data set. Now you are combining that display tool with measurement data — lengths, heights, or other quantities that students actually measured. This pairing matters because measurement data often has many different values spread across a range, and a line plot reveals the shape of that spread in a way a list of numbers never could.
To create a line plot, start by collecting or being given a set of measurements. Draw a number line that spans from the smallest to the largest value, with marks for each value that might appear. Then go through the data one observation at a time and place an X above the matching position. If three students measured a bean plant at 7 centimeters, there will be three Xs stacked above the 7. When you are done, every data point is represented, and the stack heights show you which values are most common.
To interpret a line plot, ask four questions: Where are the values clustered? That is where most of the data sits. Are there gaps — values with no Xs? Gaps suggest something divides the group into subsets. Are there outliers — isolated values far from the rest? Outliers are worth investigating. What is the range — the distance from the smallest to the largest value? A wide range means lots of variation; a narrow range means most measurements were similar.
Line plots connect data collection to visual reasoning. When a class measures the lengths of different caterpillars and plots the results, the line plot answers questions that looking at the raw numbers cannot easily answer: Are most caterpillars about the same length, or is there a wide spread? Are there any unusually long or short ones? This visual summary of measurement data is a foundation for more advanced data analysis you will encounter in later grades — where the same questions of center, spread, and shape will be answered with more powerful tools.