Week 4 — Readings & Resources · Exploring Relationships

Course: Introduction to Statistics (MATH 11) · Silver Oak University (fictional sample) · Prof. Rivera
Objective covered: Objective 3 — Describe the relationship between two variables.

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.

This week's load is deliberately light: 4 short readings + 3 short videos, grouped by the four ideas from the lecture. Read or watch one item per group and you're ready for the quiz; do all of them and you'll be very comfortable. Total time is roughly 45–55 minutes if you do everything, far less if you pick one per group.

Reading order that matches the lecture: ① scatterplots — direction, form, strength → ② correlation r (what it does and doesn't mean) → ③ two-way tables (marginal & conditional proportions) → ④ lurking variables & correlation ≠ causation.

A habit to start now: before you trust any "X is linked to Y" item below, you already know the move from class — picture it, measure it, then ask who else is in the room. Keep that in mind as you read.

① Scatterplots · Direction, Form, Strength

Maps to Lecture Segment 2. Say all three words every time — D-F-S: Direction, Form, Strength — and always ask "any outliers?"

Reading — "Exploring bivariate numerical data" (Khan Academy unit)
🔗 https://www.khanacademy.org/math/statistics-probability/describing-relationships-quantitative-data
Why it's assigned: the unit that walks the whole week's first half — building a scatterplot, describing its direction/form/strength, and reading correlation — with worked examples and quick practice. Start at the top ("Introduction to scatterplots").
⏱ ~8 min for the intro sections

Video — "Constructing a scatter plot" (Khan Academy)
🔗 https://www.khanacademy.org/math/statistics-probability/describing-relationships-quantitative-data/introduction-to-scatterplots/v/constructing-scatter-plot
Why it earns the click: watch a scatterplot get built one point at a time from a small data table — exactly the "one dot per individual, placed by its two values" idea from Segment 2, so the picture stops being abstract.
⏱ ~5 min

② Correlation r · What It Does (and Doesn't) Mean

Maps to Lecture Segment 3. The line to carry: r is a thermometer for a straight line — it gives direction and tightness, and nothing else. Always between −1 and +1.

Reading — "Pearson Correlation Coefficient (r): Guide & Examples" (Scribbr)
🔗 https://www.scribbr.com/statistics/pearson-correlation-coefficient/
Why it's assigned: the cleanest plain-language version of what r measures — a number between −1 and +1 for the strength and direction of a linear relationship — including the traps we named in class (r misses curves; r isn't a percent). Read the "interpreting" section closely.
⏱ ~7 min

③ Two-Way Tables · Marginal & Conditional Proportions

Maps to Lecture Segment 5. Remember the flag: the word "given" (or "of the …") names the denominator. Marginal = ÷ grand total; conditional = ÷ one group's total.

Reading — "Two-way tables review" (Khan Academy, article)
🔗 https://www.khanacademy.org/math/ap-statistics/analyzing-categorical-ap/two-way-tables-summarizing-categorical-data/a/two-way-tables-review
Why it's assigned: a tight, example-driven walk through building a two-way table and pulling row, column, and total counts out of it — the foundation for the proportions we compute in Segment 5.
⏱ ~5 min

Video — "Marginal and conditional distributions" (Khan Academy)
🔗 https://www.khanacademy.org/math/ap-statistics/analyzing-categorical-ap/distributions-two-way-tables/v/marginal-distribution-and-conditional-distribution
Why it earns the click: the single clearest demo of the difference that trips everyone up — a marginal proportion (divide by the grand total) versus a conditional one (divide by one group's total) — worked live on a real table. This is the heart of the quiz's table item.
⏱ ~6 min

④ Lurking Variables · Correlation ≠ Causation

Maps to Lecture Segment 7. The line to carry out of this week: when X and Y move together, always ask who else is in the room — only a randomized experiment (or a ruled-out lurking variable) earns a causal arrow.

Reading — "Confounding Variables: Definition, Examples & Controls" (Scribbr)
🔗 https://www.scribbr.com/methodology/confounding-variables/
Why it's assigned: explains the "third-variable" problem precisely — a confounding/lurking variable that influences both the supposed cause and the supposed effect — which is exactly why the ice-cream-and-drowning correlation isn't a cause.
⏱ ~6 min

Video — "Correlation Doesn't Equal Causation: Crash Course Statistics #8"
🔗 https://www.youtube.com/watch?v=GtV-VYdNt_g
Why it earns the click: the liveliest tour of the whole week — it introduces the scatterplot, defines correlation and r, and lands hard on why two things moving together isn't proof one causes the other. If you watch one video this week, watch this.
⏱ ~12 min

Optional one-stop reference (free online text)

If you'd like one optional reference to skim, OpenIntro Statistics keeps its full text and per-section videos free to read online. The early chapters cover summarizing two variables and the relationship ideas in this week; Chapter 8 ("Introduction to Linear Regression") is where these same ideas — scatterplots, residuals, and correlation — get formalized in a later week.
🔗 https://www.openintro.org/book/os/
Why it's here: a reputable, currently-available reference you can return to in later weeks — entirely optional this week.

Pick-one quick path (≈18 min total)

In a hurry? Do exactly these four and you'll be ready for the quiz:
1. Skim the "Introduction to scatterplots" sections of the Khan unit (group ①).
2. Read the interpreting section of Pearson Correlation Coefficient (r) (group ②).
3. Watch Marginal and conditional distributions (group ③).
4. Watch Crash Course #8 — Correlation Doesn't Equal Causation (group ④).

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