Title: Week 17 — Readings & Resources · Linear Regression with Inference + Course Synthesis
Course: Introduction to Statistics (18-Week Generic Edition)

Course: Introduction to Statistics (18-week generic edition) Objective covered: Objective 8 — simple linear regression and inference for the slope (plus the synthesis review of the whole inference toolkit). Your primary reading is Chapter 17 (in this module). Everything below is the optional, go-deeper layer.

How to use this page

Everything here is a link to an external resource — open it in your browser, the same way you'd open a YouTube link. Nothing needs to be downloaded, and nothing costs money.

The load is deliberately light: 4 short readings + 4 short videos, grouped by the week's big ideas. Read or watch one item per group and you're well prepared; do all of them and you'll be very comfortable. Total time is roughly 60–70 minutes if you do everything, far less if you pick one per group.

Order that matches the chapter and lecture: ① the least-squares line → ② r², residuals & the limits of the line → ③ inference for the slope → ④ choosing the right procedure (the course synthesis).

A habit for your last instructional week: every time a resource shows you a fitted line, ask the week's three questions — 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.

① The Least-Squares Line

Maps to Chapter 17, Section 1 and Lecture Segment 2. The line ŷ = a + bx is a promise about averages: b = r·(s_y ⁄ s_x), and the line always passes through (x̄, ȳ).

Reading — Simple Linear Regression: An Easy Introduction & Examples (Scribbr) 🔗 https://www.scribbr.com/statistics/simple-linear-regression/ Why it's assigned: the cleanest plain-language tour of the model — what the slope and intercept mean, the conditions behind the line, and a worked example read straight off output, exactly this course's depth. ⏱ ~8 min

Video — Simple Linear Regression: The Least Squares Regression Line (jbstatistics) (video, ~7 min, captioned) 🔗 https://www.youtube.com/watch?v=coQAAN4eY5s Why it earns the click: shows visually what "least squares" minimizes — the squared vertical misses — so the line stops being a formula and becomes an obvious choice.

Video — Regression: Crash Course Statistics #32 (CrashCourse) (video, ~13 min, captioned) 🔗 https://www.youtube.com/watch?v=WWqE7YHR4Jc Why it earns the click: the liveliest end-to-end pass through regression — line, slope, predictions, and the test of the slope — in one sitting; ideal before or after the week's lectures.

② r², Residuals & the Limits of the Line

Maps to Chapter 17, Section 2 and Lecture Segments 3–4. The hooks: r² is a share, not a grade — and outside the data, the line is fiction.

Reading — Residuals (Statistics By Jim) 🔗 https://statisticsbyjim.com/regression/residuals/ Why it's assigned: a crisp entry on the week's workhorse quantity — residual = observed − predicted — and why residual patterns, not r alone, judge a model. ⏱ ~5 min

Reading — Coefficient of Determination (R²): Calculation & Interpretation (Scribbr) 🔗 https://www.scribbr.com/statistics/coefficient-of-determination/ Why it's assigned: nails the correct reading of r² — the proportion of variation explained — with the high-vs-low r² scatterplots that make the idea visible. ⏱ ~6 min

Video — R-squared, Clearly Explained!!! (StatQuest with Josh Starmer) (video, ~11 min, captioned) 🔗 https://www.youtube.com/watch?v=2AQKmw14mHM Why it earns the click: builds r² from "variation around the mean vs. variation around the line" — after this, "81% of variation explained" stops being a slogan and becomes a picture.

③ Inference for the Slope

Maps to Chapter 17, Section 3 and Lecture Segments 5–6. H₀: β = 0 is the flat line; t = b ⁄ SE(b) with df = n − 2 — the same t logic you've run since Week 11.

Video — Introduction to inference about slope in linear regression (Khan Academy) (video, ~8 min, captioned) 🔗 https://www.youtube.com/watch?v=Pzglbt9_xCI Why it earns the click: shows the sampling-variability story behind the slope — many samples, many tilted lines — which is exactly the picture that makes H₀: β = 0 and SE(b) meaningful.

④ Choosing the Right Procedure (Course Synthesis)

Maps to Chapter 17, Section 4 and Lecture Segment 7. The hook: mean, proportion, counts, or a line? Name the answer's shape, and the procedure names itself.

Reading — Choosing the Right Statistical Test: Types & Examples (Scribbr) 🔗 https://www.scribbr.com/statistics/statistical-tests/ Why it's assigned: a decision flowchart spanning the tests you now own — comparison tests, proportion tests, chi-square, and regression — ideal for the synthesis drill and as a final-review companion. (It also names tests beyond this course, like ANOVA — recognize them as "out of scope" and move on.) ⏱ ~8 min

Optional one-stop reference (free online text)

If you'd like one optional reference to close out the term, OpenStax Introductory Statistics 2e keeps its full text free to read online. Chapter 12 (Linear Regression and Correlation) covers this week end to end — the line of best fit, r and r², prediction, and testing the relationship. 🔗 https://openstax.org/books/introductory-statistics-2e/pages/12-introduction Why it's here: a reputable, currently-available reference for this week — and a good chapter-by-chapter review shelf as you head into the Week 18 final.

Pick-one quick path (≈25 min total)

In a hurry? You've read Chapter 17 — then do exactly these three and you'll be ready for the quiz: 1. Watch Regression: Crash Course Statistics #32 (group ①). 2. Watch R-squared, Clearly Explained!!! (group ②). 3. Read Choosing the Right Statistical Test (group ④) — it doubles as final-exam prep.

Heads-up (links rot): these point to outside sites that occasionally move or rename pages. If a link ever fails, tell your instructor and use the OpenStax reference above in the meantime.