A scatter plot is a graph of bivariate quantitative data where each point represents one observation, with the explanatory variable on the x-axis and the response variable on the y-axis. On the AP Stats exam, you describe it using form, direction, strength, and unusual features.
A scatter plot displays bivariate quantitative data, meaning two numeric measurements taken on the same individuals. Each point gets an x-coordinate from one variable and a y-coordinate from the other. By convention, the explanatory variable (the one used to explain or predict) goes on the x-axis, and the response variable goes on the y-axis. So if you're studying whether hours studied predicts exam score, hours studied is x and score is y.
The real AP skill isn't making the plot, it's describing it. The CED gives you a four-part checklist: form (linear or non-linear), direction (positive or negative association), strength (how tightly the points follow the pattern), and unusual features (outliers, clusters, or changing variability). A positive association means y tends to increase as x increases; a negative association means y tends to decrease as x increases. Notice the word "tends." Real data is messy, and a scatter plot lets you see the trend and the scatter around it at the same time.
Scatter plots live in Topic 2.4 of Unit 2 (Exploring Two-Variable Data) and anchor two learning objectives. AP Stats 2.4.A asks you to represent bivariate quantitative data with a scatterplot, and AP Stats 2.4.B asks you to describe its characteristics. That second one is where points are won and lost. Everything else in Unit 2 builds on this graph. The correlation coefficient puts a number on the strength and direction you see, and the least-squares regression line formalizes the linear form. If you can't read a scatter plot, the rest of the unit is numbers without meaning. It's also your first check on any regression question: a strong correlation means nothing if the scatter plot shows a curve.
Keep studying AP® Statistics Unit 5
Correlation Coefficient (Unit 2)
The correlation coefficient r is basically a scatter plot compressed into one number. It quantifies the direction and strength of a linear association, but only the scatter plot can tell you whether the form is actually linear in the first place.
Explanatory Variable (Unit 2)
The explanatory variable claims the x-axis, and the response variable gets the y-axis. Getting this assignment right matters because regression predicts y from x, not the other way around.
Positive Association (Unit 2)
Direction is the first thing your eye catches on a scatter plot. Points trending up and to the right show a positive association, meaning both variables tend to increase together.
Bivariate Analysis (Unit 2)
The scatter plot is the starting move of all bivariate analysis. Just like you'd never compute a mean without glancing at a dotplot or histogram, you should never compute r or fit a regression line without looking at the scatter plot first.
Multiple-choice questions hand you a described or pictured scatter plot and ask you to name what you see. Practice questions in this style include spotting increasing variability in reaction times as age increases, recognizing a non-linear rise-then-fall pattern in rainfall versus crop yield, and identifying outliers that sit far from an otherwise tight positive linear pattern. On FRQs, scatter plots show up as the setup for regression questions. The 2018 FRQ Q1, for example, gave bivariate data on customers in a checkout line and checkout time. When asked to describe a scatter plot, hit all four elements (form, direction, strength, unusual features) in context. "Strong positive linear association between hours studied and exam score, with one outlier at (2, 95)" earns credit. "The points go up" does not.
A scatter plot graphs the raw data, response variable versus explanatory variable, and you read its form, direction, and strength. A residual plot graphs the leftover prediction errors after fitting a regression line, and you read it for one thing only: whether a linear model is appropriate. A good residual plot looks like random scatter with no pattern. So a clear pattern is what you want in a scatter plot and exactly what you don't want in a residual plot.
A scatter plot shows bivariate quantitative data with one point per observation, explanatory variable on the x-axis and response variable on the y-axis.
Describe every scatter plot using four elements: form (linear or non-linear), direction (positive or negative), strength (weak to strong), and unusual features (outliers, clusters, or changing variability).
A positive association means y tends to increase as x increases; a negative association means y tends to decrease as x increases.
Always describe a scatter plot in context, naming the actual variables, because generic answers like "the points go up" lose credit on FRQs.
Look at the scatter plot before trusting any correlation coefficient, since r only measures linear strength and can hide a curved pattern.
It's a graph of two quantitative variables measured on the same individuals, with each observation plotted as a point. The explanatory variable goes on the x-axis, the response variable on the y-axis, and you describe the result by form, direction, strength, and unusual features (LO 2.4.A and 2.4.B).
No. Strength and form are separate things. A scatter plot can show a very strong curved (non-linear) pattern, like crop yield rising sharply with rainfall, leveling off, then declining. That's strong but not linear, which is exactly why you check form before computing r.
A scatter plot graphs the original data and you want to see a pattern. A residual plot graphs the errors left over after fitting a regression line, and you want it to look patternless. A curve in the residual plot is a red flag that a linear model doesn't fit.
The explanatory variable, the one whose values are used to explain or predict the other variable. The response variable goes on the y-axis. In a hours-studied versus exam-score plot, hours studied is x and exam score is y.
Form (linear or non-linear), direction (positive or negative), strength (how tightly points follow the pattern), and unusual features (outliers, clusters, changing variability). Mention all four, in the context of the variables, to earn full credit.
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