Title: Week 17 Overview — Start Here
Course: Introduction to Statistics (18-Week Generic Edition)

Welcome to Week 17: Linear Regression with Inference + Course Synthesis

This is your home base for the week. Read it first, then work the checklist below from top to bottom. Everything you need is linked inside the module.

This is the last week of new material — and it's the week the course shakes hands with itself. You've been able to see a two-variable relationship since Week 4 (scatterplots, the correlation r). This week you finally get to use one: fit the least-squares line, read its slope like a professional, and then put that slope on trial with the same t-machinery you've run since Week 11. Then we zoom out: with every tool now on the table, the final skill is choosing the right one — which is exactly what next week's cumulative final rewards.

The week's big question

"When can a line drawn through yesterday's data be trusted to predict tomorrow — and how do we tell a real slope from a lucky tilt?"

By the end of the week you'll ask three questions of any fitted line you meet: What does the slope say, in units? How much does the line explain (r²)? And where does the data's range end — because beyond it, the line is fiction.

By the end of this week, you can…

Use this as a checklist. If you can do all five out loud, you're ready for the quiz — and most of the way ready for the final.

- [ ] Interpret a slope and intercept in context — the full four-part sentence: per one unit of x, predicted, on average, in y's units (and know when an intercept deserves no interpretation at all).
- [ ] Read r² as a share of variation explained (never an accuracy rate) and a residual as actual − predicted.
- [ ] Judge a residual plot (patternless = good; curve or fan = trouble) and refuse to extrapolate beyond the fitted range.
- [ ] Run the t-test and confidence interval for the slope from computer output — t = b ⁄ SE(b), df = n − 2, H₀: β = 0 ("the flat line") — and use the interval-contains-zero duality.
- [ ] Choose the right procedure for any scenario from Weeks 11–17: mean, proportion, counts, or a line?

What to do this week, in order

The table below lists the week's items in working order, with what each is worth and when it's due.

# | Do this | Type | Due
1 | Read Chapter 17 — the module's primary reading | Chapter (ungraded prep) | Early in the week
2 | Skim the slides (Deck 17) and the Week 17 lecture outline; browse the Readings & Resources links that interest you | Prep (ungraded) | Alongside class
3 | Lecture Tutorial 17 — work the line, r², slope inference, and the choose-the-procedure map with your chatbot; submit the share link + Completion Summary | Tutorial · graded (Lecture tutorials, 20% group) | End of Week 17
4 | Practice exercises — quick reps with the AI coach | Practice · ungraded | Before the quiz (recommended)
5 | Data Lab 17 — "The Capstone Line: Predicting a Penguin" — fit the flipper-length → body-mass regression on the Week 1 penguins with =SLOPE, =INTERCEPT, =RSQ | Data lab · graded (Data labs, 15% group) | End of Week 17
6 | Quiz 17 — slope/intercept, r², residuals, extrapolation, slope inference, choosing the procedure | Quiz · graded (Quizzes, 15% group) · closed to AI | End of Week 17
7 | Discussion 17 — "The Tool You'll Keep" — defend the one tool you'll actually use after this course, and stress-test a prediction from your own life; post the AI summary + chat link | Discussion · graded (Discussions, 15% group) | Initial post two days before week's end; replies by end of Week 17
8 | Assignment 17 — "The Slope on Trial" — four AI-coached problems ending in the synthesis; submit the report (score on line 1) + chat link | Assignment · graded (Assignments, 25% group) | End of Week 17

Heads-up on the AI work: in this course the chatbot drafts, and you judge. Regression is where chatbots bluff hardest — they'll call r² "the correlation," read a meaningless intercept with a straight face, and extrapolate to a 300 mm flipper without blinking. This week's lab bait is designed to make yours do exactly that — catching it is graded.

Late policy reminder: 10% off per day late. If life happens, reach out to your instructor before the deadline — early is always easier.

How to succeed this week

- Memorize the four-part slope sentence. Per one unit of x · predicted · on average · in context units. Every slope question on the quiz, the lab, and the final is this sentence wearing different clothes.
- Keep r² humble. It's a share of variation explained — a share, not a grade — and never "percent accurate."
- Respect the data's range. Inside it, the line predicts; outside it, the line is fiction (ask the lab's 300 mm penguin).
- Reuse your t-skills. The slope test is Week 14 with a new df rule: t = b ⁄ SE(b), df = n − 2, same table, same duality (interval contains 0 ⇔ fail to reject).
- Drill the decision map. Mean, proportion, counts, or a line? Name the answer's shape and the procedure names itself — that one habit is most of the final.
- A word about Week 18: the final is cumulative, 60 multiple-choice, worth 5% — a low-stakes checkpoint, exactly like the midterm. Its study guide, practice exam, and exam-prep tutorial are all in the Week 18 module, waiting. Nothing to fear; everything to review.

Seventeen weeks ago, a statistic was a number someone told you. This week it becomes a claim you can interrogate from every side. Finish strong.