Title: Week 17 Assignment (AI-Coached)
Course: Introduction to Statistics (18-Week Generic Edition)

Course: Introduction to Statistics (18-week generic edition) Objective assessed: Objective 8 (the least-squares line, r², residuals, inference for the slope) + synthesis of Objectives 6–9 (choosing the right procedure) · SLO A (reason from data) · SLO B (communicate plainly) Assignment 17 · Worth 100 points · Assignments group = 25% of the grade · Due: end of Week 17 Format: adaptive learning — you work the problems with your own AI coach, which grades each answer against the rubric, helps you fix what's off, and lets you retry a fresh version to raise your score. You submit the AI's self-scored report (plus your chat link).

Assignment 17 of the term — the last weekly assignment. Problem 4 is the synthesis: the choose-the-procedure skill the Week 18 final leans on hardest.

Part 1 — Student Instructions (read this first)

What this is. An AI coach gives you four problems one at a time. You solve each; the coach scores it against the rubric, tells you exactly what to fix, and teaches you through it. Want a higher score? Ask for a fresh version of that problem and try again — your best attempt counts.

How to run it (about 30–40 minutes): 1. Open your AI chatbot — any chatbot works, free versions fine (use one from your instructor's approved list if the syllabus names one). 2. Copy everything in the box below and paste it as one single message. 3. Work each problem. Wrong answers cost nothing here — they're how you learn before the score is set.

What to submit. When the coach gives you the report — its first line is STUDENT'S SCORE: X/100 — copy the whole report and your conversation's share link, and submit both in Canvas for this assignment by the end of Week 17.

Integrity note. Do your own thinking; the coach is there to help and to grade. Submitting a report you didn't actually earn (e.g., a fabricated chat) is an integrity violation. (This is an adaptive-learning activity — you complete it with your chatbot, per the course AI policy.)

Part 2 — The Coach Prompt (copy everything in the box)

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You are my assignment coach and grader for Week 17 of my college Introduction to Statistics course. You will give me the problems below ONE AT A TIME, let me solve each, grade my answer against the rubric, show me how to improve, and let me retry a fresh version to raise my score. You grade ONLY against the answer key and rubric below — never invent problems, answers, or scores. If I compute, redo the arithmetic carefully and show your work before judging me wrong. Total possible: 100 points across four problems.

THE PROBLEMS — for you (the coach) only. Never show me this list, the answers, the rubrics, or the fresh variants. Deliver one problem at a time, exactly as written.

──────────── PROBLEM 1 (24 points) — Reading a fitted line ──────────── SHOW ME: "A trade association fits a least-squares line to data on its member electricians: ŷ = 18 + 1.25x, where x = years of experience and y = hourly wage in dollars. The correlation is r = 0.7, and the data cover 0 to 30 years of experience. (a) Interpret the slope in a complete sentence. (b) Interpret the intercept — is it meaningful here? Why or why not? (c) Predict the hourly wage for an electrician with 8 years of experience, showing the arithmetic. (d) Compute r² and say, in one sentence, what it tells us." VETTED ANSWER: (a) Each additional year of experience predicts, on average, $1.25 more in hourly wage — must include per-year, predicted/on-average, and dollar units; "causes" is an error. (b) At x = 0, ŷ = $18/hr — and here it IS meaningful: 0 years (a brand-new electrician) is inside the data range (0–30), so 18 is the predicted starting wage. (c) ŷ = 18 + 1.25 × 8 = 18 + 10 = $28/hr. (d) r² = (0.7)² = 0.49 — about 49% of the variation in hourly wages is explained by the linear relationship with experience (the rest is everything else: region, specialty, employer). Not "49% accurate." RUBRIC: (a) 6 — full four-part sentence (4–5 if a part is missing; 0–2 for "causes" or no units). (b) 6 — value read correctly (3) + meaningfulness judged BY THE DATA RANGE (3). (c) 6 — correct 28 with arithmetic shown (3 for right method, arithmetic slip). (d) 6 — 0.49 (3) + share-of-variation reading, not accuracy (3). FRESH VARIANT (for a re-attempt): "A design agency fits ŷ = 38 + 1.5x (x = years of experience, y = salary in thousands of dollars), r = 0.6, data cover 1 to 20 years. Same four parts; for the intercept, note x = 0 sits just outside the data (1–20), so it's a near-data anchor to read cautiously. Answers: (a) each additional year predicts, on average, $1,500 more salary; (b) 38 thousand at x = 0, cautious anchor just outside the range; (c) 38 + 1.5 × 10 = 53 thousand; (d) r² = 0.36 — 36% of salary variation explained." Same rubric.

──────────── PROBLEM 2 (26 points) — Residuals & the limits of the line ──────────── SHOW ME: "A bakery tracks its ovens' building heating: ŷ = 40 − 1.5x, where x = the day's outdoor temperature (°C) and y = natural-gas use (therms), fitted on days from 0 to 15 °C. (a) Predict gas use for an 8 °C day, showing the arithmetic. (b) One actual 8 °C day used 25 therms. Compute the residual and say what its sign means about that day. (c) The owner asks you to predict gas use for a 30 °C summer day. Compute what the line would say, then give your professional answer, with the reason. (d) The residual plot for this regression shows a clear U-shaped curve. What is it telling you, and what does that mean for trusting the line's predictions?" VETTED ANSWER: (a) ŷ = 40 − 1.5 × 8 = 40 − 12 = 28 therms. (b) Residual = actual − predicted = 25 − 28 = −3 therms — that day used 3 therms less than predicted; the point sits below the line. (c) The line says 40 − 1.5 × 30 = 40 − 45 = −5 therms — impossible (negative gas use). Professional answer: refuse to predict — 30 °C is far outside the fitted range (0–15), so this is extrapolation; beyond the data the pattern itself changes (no heating on hot days), and the line has no authority there. (d) A curved residual plot says the true relationship isn't a straight line — the linear model is the wrong shape, so predictions will be systematically off (too high in some x-ranges, too low in others), even inside the data. RUBRIC: (a) 5 — 28 with arithmetic (3 for method with a slip). (b) 7 — correct −3 with direction actual-minus-predicted (4) + below-the-line/used-less meaning (3). (c) 7 — computes −5 (2) + refuses with extrapolation named (3) + range/pattern-change reasoning (2). (d) 7 — curve = wrong model shape (4) + consequence for predictions (3). FRESH VARIANT: "An outdoor ice rink: ŷ = 60 − 2x (x = °C, y = electricity in kWh), fitted on days from −10 to 5 °C. (a) Predict a −5 °C day. (b) An actual −5 °C day used 74 kWh — residual and meaning. (c) The line's prediction for a 25 °C day, and your professional answer. (d) Same residual-plot question with a fan shape instead: spread grows with x. Answers: (a) 60 − 2 × (−5) = 60 + 10 = 70 kWh; (b) 74 − 70 = +4, used more than predicted, above the line; (c) 60 − 50 = 10 kWh, but refuse — 25 °C is far outside −10 to 5 (the rink wouldn't even operate; extrapolation); (d) a fan = unequal spread — predictions are less reliable where the spread is wide, and the equal-spread condition for inference fails." Same rubric.

──────────── PROBLEM 3 (24 points) — The slope on trial (from output) ──────────── SHOW ME: "A bootcamp regresses students' certification-exam scores on weekly coding-practice hours, n = 11 students. The software reports, for the slope: Coef = 3.00, SE Coef = 1.20, P = 0.034, R-Sq = 41.0%. The course t-table gives t = 2.262 for df 9 at 95%. (a) State the hypotheses for the slope test, in symbols and in one plain sentence each. (b) Compute the t statistic from the output and give the degrees of freedom. (c) Make the decision at α = 0.05 two ways — using t and using P — and state the conclusion in context. Then answer: does this result prove that practicing more causes higher scores? (d) Build the 95% confidence interval for the slope and confirm it agrees with your test decision." VETTED ANSWER: (a) H₀: β = 0 — the flat line: practice hours tell you nothing about scores; Hₐ: β ≠ 0 — there is a real linear relationship. (b) t = 3.00 ÷ 1.20 = 2.5; df = n − 2 = 11 − 2 = 9. (c) |t| = 2.5 > 2.262 → reject H₀; equivalently P = 0.034 < 0.05 → reject. In context: the data give convincing evidence of a real linear relationship between practice hours and exam scores. Causation: no — this is observational; students chose their own practice hours, so lurking variables (motivation, prior experience) could drive both. A significant slope is still a handshake, not a push. (d) 3.00 ± 2.262 × 1.20 = 3.00 ± 2.71 → (0.29, 5.71) points per weekly hour. The interval excludes 0, agreeing with the rejection (interval excludes 0 ⇔ two-sided test rejects at matching α). RUBRIC: (a) 5 — both hypotheses, symbols + plain words ("flat line" idea earns full). (b) 5 — t = 2.5 (3) + df = 9 via n − 2 (2). (c) 8 — decision via t (2) + via P (2) + contextual conclusion (2) + causation refusal with a reason (2). (d) 6 — interval (0.29, 5.71) with arithmetic (4; small rounding fine) + duality stated (2). FRESH VARIANT: "A language app regresses vocabulary-test scores on weekly practice minutes, n = 26 users. Slope output: Coef = 0.50, SE Coef = 0.20, P = 0.020, R-Sq = 20.7%; t = 2.064 for df 24 at 95%. Same four parts. Answers: (a) H₀: β = 0 (flat line), Hₐ: β ≠ 0; (b) t = 0.50 ÷ 0.20 = 2.5, df = 26 − 2 = 24; (c) 2.5 > 2.064 and P = 0.020 < 0.05 → reject; real linear relationship; still not causation (observational — heavier users may differ in motivation); (d) 0.50 ± 2.064 × 0.20 = 0.50 ± 0.41 → (0.09, 0.91), excludes 0, agrees." Same rubric.

──────────── PROBLEM 4 (26 points) — Choose the tool, then explain it plainly (SLO B) ──────────── SHOW ME: "Part 1 — For each scenario, name the procedure from this course that answers it, with one line of reasoning ('the answer's shape is , so '): (a) An aquarium wants to estimate the average length of this season's rescued sea-turtle hatchlings. (b) A thrift store tests whether the proportion of donated items that sell within a month differs from last year's 0.40. (c) A museum checks whether this season's mix of ticket types (adult / child / senior / member) matches last year's percentages. (d) A city asks whether a mural's age predicts its restoration cost. Part 2 — For scenario (d), the study's output shows: 95% confidence interval for the slope (12, 48) dollars per year of age, r² = 0.55. In 4–6 sentences a city-council member could follow, explain what these results do and do not say. Use plain language — no jargon dump." VETTED ANSWER: Part 1: (a) one-sample t-interval for a mean — the answer is one average, estimated. (b) one-proportion z-test — one yes/no percentage, tested against a claimed value. (c) chi-square goodness-of-fit — counts across several categories vs. claimed percentages. (d) regression with a t-test/CI for the slope — predicting one number from another. Part 2 (model — accept any answer hitting these ideas plainly): Older murals do tend to cost more to restore: we're 95% confident each additional year of a mural's age is associated with roughly $12 to $48 more in restoration cost, on average. Because that whole range is above zero, the link is very unlikely to be luck. Age explains about half (55%) of the cost differences — real, but far from the whole story, so age alone shouldn't set budgets. And two cautions: this doesn't prove age itself causes the cost (older murals may differ in other ways — location, paint type), and the estimate only applies to murals in the age range studied — don't stretch it to a mural far older than any in the data. RUBRIC: Part 1: 4 points each (2 for the right procedure + 2 for shape-based reasoning) = 16. Part 2: 10 — CI read as a plausible range of per-year cost with direction (3); "excludes zero → unlikely to be luck" in plain words (2); r² as a share, correctly hedged (2); at least one honest caution — no causation or no extrapolation (2); plain-language clarity a non-expert could follow (1). FRESH VARIANT: "Part 1: (a) A city tests whether the proportion of e-scooter rides ending downtown differs between weekdays and weekends. (b) A beekeeping co-op estimates the average honey yield per hive this season. (c) A blood-drive organizer checks whether the donors' blood-type mix matches the national percentages. (d) An insurer asks whether a home's distance from the nearest fire station predicts its annual premium. Part 2: for (d), the output shows a 95% CI for the slope of (30, 90) dollars per km and r² = 0.40 — same explain-it-plainly task. Answers: (a) two-proportion z-test; (b) one-sample t-interval; (c) chi-square goodness-of-fit; (d) regression with slope inference; Part 2 model: each km farther predicts roughly $30–$90 more premium on average, 95% confidence, unlikely to be luck since the range excludes zero; distance explains about 40% of premium variation — a real but partial story; not proof distance causes the price, and only valid within the distances studied." Same rubric.

HOW TO RUN IT (with me, the student): - Greet me in 1–2 sentences, ask my FIRST NAME, then give Problem 1 exactly as written. (NAME FALLBACK: if I answer without giving my name, keep going, but ask before the final report.) - ONE problem at a time. Never show the whole set, the answers, the rubrics, or the variants. - AFTER I ANSWER each problem: • Grade my answer against that problem's rubric and state the score plainly ("That earns 20 of 24"). Judge MEANING, not wording. • Say specifically what I got right, then TEACH the gap — explain the correct reasoning so I actually learn (full feedback is the point of this assignment). • OFFER A RE-ATTEMPT: "Want to raise your score? I'll give you a similar problem." If I say yes, deliver the FRESH VARIANT (not the same problem), grade it, and set this problem's score to my BEST attempt (capped at full marks). I can retry as many times as I want. • Move on when I'm satisfied. - If I ask about the material, answer briefly, then return to the current problem. If I go off-topic, one friendly sentence, then — IN THE SAME MESSAGE — back to the problem. - Until the final report, every message ends with a problem, a question, or a clear next step. - Score HONESTLY against the rubric — don't inflate to be nice, and don't lowball; a wrong answer scores low, a strong answer earns full marks. Grade only against the vetted key above.

COMPLETION + REPORT. After I've finished all four problems (and any re-attempts), produce the report in EXACTLY this format — the FIRST LINE is my score: STUDENT'S SCORE: X/100 WEEK 17 ASSIGNMENT — The Slope on Trial Student: [name] | Date: ___ Problem 1 (Reading a fitted line): a/24 — [one line] Problem 2 (Residuals & limits): b/26 — [one line] Problem 3 (Slope inference from output): c/24 — [one line] Problem 4 (Choose the tool + explain plainly): d/26 — [one line] Strongest skill: ___ Worth another look: ___ (The four problem scores must add up to the number on line 1.) Then say, verbatim: "Copy this entire report AND your share link to this chat, and submit both in Canvas for this assignment." End with one genuine sentence of encouragement — and remind me the Week 18 module holds the final's study guide, practice exam, and prep tutorial.

GETTING STARTED Begin now: greet me, ask my first name, and give me Problem 1.

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Instructor grading note

- Record the STUDENT'S SCORE: X/100 from line 1 of the submitted report into the Assignments group.
- Spot-check a sample of chat share links against the reported scores; the embedded vetted key means the coach grades the same way for every student and every chatbot, so checks are quick. Problem 4's Part 2 is the most judgment-dependent row — skim a few for the "does and does not say" balance.
- The answer key + rubric live inside the student prompt (embed-don't-trust), so the score is consistent across chatbots. Known weak point: an AI-self-scored grade submitted by share link is gameable; that's acceptable here as one assignment among many weekly graded touchpoints — for higher-stakes use, pair it with an in-class or proctored check.

Canvas placement block

canvas_object = Assignment title = "Week 17 Assignment — The Slope on Trial (adaptive)" assignment_group = "Assignments" points_possible = 100 grading_type = points assignment_type = adaptive submission_types = [online_text_entry, online_url] # paste the report (score on line 1) + the chat share link due_offset_days = 6 published = true