AP Stats · Unit 2 Progress Check

AP Statistics Unit 2 Progress Check Exploring Two-Variable Data walkthrough.

AP Statistics Unit 2 is two-variable data, and the Unit 2 Progress Check MCQ is built on scatterplots, correlation, and least-squares regression. This walkthrough covers what each question type tests, the interpretation traps, and how to reason through them. Explanations only — we never publish AP Classroom answer keys.

Updated August 2026Written by Mahmudul HasanFree · No signup

What Unit 2 covers

Unit 2 is Exploring Two-Variable Data. Exploring two-variable data is roughly 5–7% of the AP Statistics exam and the foundation for inference on slopes later.

Scatterplots

Describing form, direction, strength, and unusual points in a relationship.

Correlation r

What it measures, its range from −1 to 1, and why it only captures linear association.

Least-squares regression

The line that minimises squared residuals, and interpreting slope and intercept in context.

Residuals and residual plots

Reading a residual plot to judge whether a linear model fits.

r-squared

The proportion of variation in y explained by the model.

Influential points and outliers

How high-leverage points and outliers pull the regression line.

The Progress Check MCQ: what each question type tests

These are the question patterns that recur on this Progress Check, and what each one is really asking.

Interpret the slope

Slope is the predicted change in y per one-unit change in x — stated with both variables’ names and units.

Read a residual plot

A clear pattern means the linear model is wrong. Random scatter supports it.

Correlation reasoning

r near zero means weak linear association, not no relationship — a strong curve can have r near zero.

r-squared interpretation

The percentage of variation in the response explained by the explanatory variable.

Effect of an influential point

Removing a high-leverage point can swing the slope substantially.

The Progress Check FRQ

Unit 2 free response usually gives computer regression output and asks you to interpret and predict. Points come from interpreting slope and r-squared in context, using the equation to predict, and judging model fit from the residual plot rather than from r alone.

Where students lose the most points

Confusing correlation with causation

A strong r does not establish that x causes y.

Interpreting slope without units

Slope is a rate of change and needs both variables named.

Assuming r near zero means no relationship

It means no linear relationship; a curved pattern can still be strong.

Extrapolating beyond the data

Predictions outside the observed x-range are unreliable.

Reading r-squared as r

They are different: r-squared is the square of correlation and is always between 0 and 1.

How to work through this unit

For any regression question, interpret the slope as “for each additional [x unit], the model predicts [change] in [y].” Getting that sentence right earns the point and prevents the causation trap.

Once you have finished the Progress Check, put your raw score into our AP Statistics Calculator to see roughly where that pace puts you on the 1–5 scale, and use the AP Statistics Review for the full exam format and study plan.

Frequently asked questions

Quick answers — written by humans, not a chatbot.

What does the AP Statistics Unit 2 Progress Check cover?

Two-variable data: scatterplots, correlation, least-squares regression, residuals and residual plots, r-squared, and influential points and outliers.

How do I interpret the slope of a regression line?

As the predicted change in the response variable for each one-unit increase in the explanatory variable, stated with the names and units of both variables.

Does a correlation near zero mean there is no relationship?

No. It means there is no strong linear relationship. A strongly curved pattern can still have a correlation near zero.

Do you post AP Statistics Unit 2 answer keys?

No. AP Classroom work is graded as your own, so we publish walkthroughs of the reasoning instead.