AP Statistics
2A–2C Free Response
Shared by Ashton · Nov 8, 2026 · 0 plays
Free-response practice for quiz 2A–2C, with a rubric and a model answer for each: two-way tables and conditional distributions, association vs. causation, interpreting probability, probability models, the complement and addition rules, and Venn diagrams. 20 questions.
20 questions across 3 sections. Write your answer, then check it against the rubric and score yourself.
Sample questions
Open an item to see its answer.
Find the marginal relative frequency of club membership and the joint relative frequency of being a member who owns a snowmobile. Show your work.
Sample answer: Members total 212 + 77 + 16 = 305, so the marginal relative frequency is 305/1526 ≈ 0.200 (20.0% of visitors are members). The joint relative frequency of member and owner is 16/1526 ≈ 0.0105, about 1% of visitors.
Find the proportion of snowmobile renters who are not club members, and explain why it is a conditional relative frequency.
Sample answer: There are 497 + 77 = 574 renters, and 497 of them are not members: 497/574 ≈ 0.866. It is conditional because we only look at visitors who meet a condition (being a renter), so the renter total is the denominator, not 1526.
Calculate the conditional distribution of snowmobile use for members and for non-members.
Sample answer: Non-members: 445/1221 = 36.4% never, 497/1221 = 40.7% rented, 279/1221 = 22.9% owned. Members: 212/305 = 69.5% never, 77/305 = 25.2% rented, 16/305 = 5.2% owned. Each set adds to 100% (99.9% for members because of rounding).
Is there an association between club membership and snowmobile use for these visitors? Justify with numbers.
Sample answer: Yes. Members were much more likely to have never used a snowmobile (69.5%) than non-members (36.4%), and much less likely to own one (5.2% vs 22.9%). Since the distributions of snowmobile use differ, knowing whether a visitor is a club member helps predict their snowmobile use.
Calculate the percent of games won after each amount of rest, and describe what a segmented bar chart of these data would show.
Sample answer: No rest: 7/15 = 46.7% wins. Short rest: 25/48 = 52.1%. Long rest: 12/19 = 63.2%. A segmented bar chart would have one 100% bar for each amount of rest, split into win and loss, and the win segment would get taller from no rest to short rest to long rest.