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2.10 Multiple Choice Questions

4 min readdecember 4, 2021


AP Statistics 📊

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Multiple Choice Practice for Exploring Two-Variable Data

Welcome to Unit 2 AP Statistics Multiple Choice Questions! Grab some paper and a pencil 📄 to record your answers as you go. You can see how you did on the Unit 2 Practice Questions Answers and Review sheet once you're done. Don't worry, we have tons of resources available if you get stumped 😕 on a question. And if solo study is not your thing, join a group in Hours! Not ready to take a quiz yet? Check out the Intro to Unit 2.
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Facts about the test: The AP Statistics exam has 40 multiple choice questions and you will be given 1 hour 30 minutes to complete the section. That means it should take you around 11 minutes to complete 5 questions.
*The following questions were not written by College Board and although they cover information outlined in the AP Statistics Course and Exam Description the formatting on the exam may be different.

1. After completing a research project on the correlation between AP Stats grade and ACT score, you receive the report that the r value is 1.42. What should you do with this data?
A. Establish that the two variables are strongly correlated
B. Establish that the two variables are not correlated
C. Establish that the two variables are moderately correlated
D. Find a new statistician
2. What type of chart is best for summarizing two categorical variables?
A. Two way table
B. Scatterplot
C. Dot Plot
D. Stem and Leaf Plot
3. What type of variable will we see along the x-axis of a scatterplot?
A. Response Variable
B. Explanatory Variable
C. Confounding Variable
D. Individualistic Variable

4. Which of the following is not a part of describing a scatterplot?
A. Form
B. Direction
C. Union
D. Strength
5. What value from a computer output will tell us how strongly the points follow a linear correlation?
A. S
B. Slope
C. r-value
D. R^2 Value
6. In a linear regression model, which variable is predicted?
A. r-value
B. x-value
C. y-value
D. z-value
7. What is the term for when a linear regression model gives us data within a certain range and we use that data to make a prediction for an x value outside of that range?
A. Extrapolation
B. Intrapolation
C. Interpolation
D. Hyperpolation
8. When looking at a residual plot for a linear pattern, what should we see?
A. A parabolic pattern
B. A curved pattern
C. A linear pattern
D. No apparent pattern
9. What does a positive residual value tell us?
A. The actual data value was lower than the prediction from our linear regression model.
B. The actual data value was higher than the prediction from our linear regression model.
C. The actual data value was equal to the prediction from our linear regression model.
D. Our predicted value was a very good prediction based on other points.
10. If the slope of our regression equation is -0.23, what does this tell us about the r value of our model?
A. It is 0.23
B. It is weak
C. It is moderate
D. It is negative
11. Which of the following would be a correct interpretation of a slope of 1.42?
A. As the x value increases by 1, the y value is predicted to increase by 1.42.
B. If our x value is 0, then the predicted y value is 1.42.
C. There is 1.42 x value for every 1 y value.
D. The line increases by 1.42.
12. What is the best way to determine if a point is an outlier from a linear regression model?
A. It has a large residual
B. It has a large x value
C. It has a large y value
D. It doesn't fit
13. A study is conducted to determine the effectiveness of different types of fertilizer on grass in the Midwest. Which of the following is the response variable?
A. Fertilizer types
B. Location
C. Rainfall
D. Growth of grass
14. Which algebraic process is often used for nonlinear models to transform our data to make it fit a linear pattern?
A. Natural log all x values
B. Natural log all y values
C. Square root all x and y values
D. Scale every value by 10 points
15. Which of the following gives a generic interpretation of an R^2 value?
A. There is an R^2% probability that the line contains any given actual data point.
B. R^2% of the points in our data set fall on the linear pattern.
C. R^2% of the variation in (y-value) is explained by its linear relationship with (x-value).
D. For any given sample size, approximately R^2% of the points can be predicted with the linear model calculated.

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👆Unit 1 – Exploring One-Variable Data
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