Title: Week 4 — Readings & Resources · Relationships Between Two Variables
Course: Introduction to Statistics (18-Week Generic Edition)

Course: Introduction to Statistics (18-week generic edition) Objective covered: Objective 3 — Describe and interpret relationships between two variables using scatterplots, correlation, and two-way tables. Your primary reading is Chapter 4 (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 four big ideas of the week. 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 75–85 minutes if you do everything, far less if you pick one per group.

Order that matches the chapter and lecture: ① scatterplots (direction, form, strength) → ② the correlation coefficient r → ③ two-way tables, marginal & conditional distributions → ④ association vs. causation & lurking variables.

A habit to start now: every time one of these resources shows you a relationship, ask the week's three questions — What does the link look like? How strong is it? Is it a cause?

① The Scatterplot · Direction, Form, Strength

Maps to Chapter 4, Section 1 and Lecture Segment 2. The reading order that never fails: Direction, Form, Strength — then check for Stragglers.

Reading — Scatterplots: Using, Examples, and Interpreting (Statistics By Jim) 🔗 https://statisticsbyjim.com/graphs/scatterplots/ Why it's assigned: a gallery of real scatterplot shapes — positive, negative, curved, clustered, outlier-spiked — so your eye learns every pattern the week's vocabulary names. ⏱ ~8 min

Video — Bivariate relationship linearity, strength and direction (Khan Academy) (video, ~9 min, captioned) 🔗 https://www.youtube.com/watch?v=30LcZqRfPRY Why it earns the click: walks through classifying real scatterplots by exactly our three questions — direction, form, strength — the precise skill Quiz 4 asks for.

② The Correlation Coefficient r

Maps to Chapter 4, Section 2 and Lecture Segment 3. The hook: sign = direction, size = strength — and r speaks only straight-line.

Reading — Correlation (JMP Statistics Knowledge Portal) 🔗 https://www.jmp.com/en_us/statistics-knowledge-portal/what-is-correlation.html Why it's assigned: a clean visual tour of r from −1 to +1, with scatterplots at each strength level and an honest section on what r cannot see (curves, outliers). ⏱ ~6 min

Video — Pearson's Correlation, Clearly Explained!!! (StatQuest with Josh Starmer) (video, ~19 min, captioned) 🔗 https://www.youtube.com/watch?v=xZ_z8KWkhXE Why it earns the click: the friendliest deep dive on what r actually measures and why the same r can hide different-looking clouds — watch it and the "r must agree with your eyes" habit becomes permanent.

③ Two-Way Tables · Marginal & Conditional Distributions

Maps to Chapter 4, Section 3 and Lecture Segment 5. The whole skill is the denominator: marginal = out of everyone; conditional = out of one group.

Reading — Contingency Table: Definition, Examples & Interpreting (Statistics By Jim) 🔗 https://statisticsbyjim.com/basics/contingency-table/ Why it's assigned: builds a two-way table from scratch ("contingency table" is the formal name), then walks marginal and conditional percentages with worked examples that mirror our aquarium table. ⏱ ~8 min

Video — Marginal and conditional distributions (Khan Academy) (video, ~11 min, captioned) 🔗 https://www.youtube.com/watch?v=Iw9fEYIpPMA Why it earns the click: computes both kinds of distribution from one table, slowly, with the denominators color-traced — the exact skill the lab's species × island table grades.

④ Association vs. Causation · Lurking Variables

Maps to Chapter 4, Section 4 and Lecture Segment 6. The line to carry: correlation is a handshake, not a push — and before you believe a push, hunt the third hand.

Reading — Correlation vs Causation (JMP Statistics Knowledge Portal) 🔗 https://www.jmp.com/en_us/statistics-knowledge-portal/what-is-correlation/correlation-vs-causation.html Why it's assigned: short and sharp on why observational associations can't prove cause, with a lurking-variable example (exercise, skin cancer, and the sunshine behind both) that pairs perfectly with the chapter's lemonade-and-sunburn story. ⏱ ~5 min

Video — Correlation and causality (Khan Academy) (video, ~11 min, captioned) 🔗 https://www.youtube.com/watch?v=ROpbdO-gRUo Why it earns the click: dissects a real-style headline claim step by step, showing how the same data support several different arrows — the week's three-reasons framework in action.

Optional one-stop reference (free online text)

If you'd like one optional reference to skim, OpenStax Introductory Statistics 2e keeps its full text free to read online. Chapter 12 (Linear Regression and Correlation) opens with scatterplots and the correlation coefficient — this week, read just those opening sections and stop before the regression-line material (that machinery is ours in Week 17). 🔗 https://openstax.org/books/introductory-statistics-2e/pages/12-introduction Why it's here: a reputable, currently-available reference you can return to in later weeks — entirely optional this week.

Pick-one quick path (≈25 min total)

In a hurry? You've read Chapter 4 — then do exactly these three and you'll be ready for the quiz: 1. Read Correlation (group ②). 2. Watch Marginal and conditional distributions (group ③). 3. Read Correlation vs Causation (group ④).

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.