Week 4 · Readings & resources

Week 4 — Readings & Resources

Introduction to Statistics Free sample edition

Your primary reading is Chapter 4 (in this module). Everything below is the optional, go-deeper layer — pick what helps, skip the rest.

How to use this page

This page has 11 items (5 readings and 6 videos). Allow 83–97 minutes for everything. Each group points back to the matching chapter section and class-session segment, so use the full menu when you want more practice, or take the quick path below.

Every video here has captions: turn them on with the CC button in the player. The chapter covers the same ideas in text.

Pick-one quick path (18–21 min total)

① Is there a pattern, and how strong is it? Scatterplots and the correlation r

Chapter 4, Section “Put one number on a straight line: the correlation coefficient” · Session 1, Segment 2, “Scatterplots and the correlation coefficient” · Remember: describe the scatterplot first, then let r put a number on a straight-line pattern only.

Scatter Plots and Correlation — Phillips Math — a short first look at positive and negative patterns on a scatterplot and how r measures their strength. (video, 4 min) (captions: automatic)
Pearson's Correlation, Clearly Explained!!! — StatQuest with Josh Starmer — a patient, picture-first walk through what a correlation means, how to read it, and where it falls short; it leans on variance, so skip any part that loses you. (video, 19 min) (captions: automatic)
Interpreting Correlations: An Interactive Visualization — R Psychologist — an interactive scatterplot: move the slider between negative 1 and 1 and watch the points close in on a straight line as r moves away from 0, then drag one point far from the rest to see how much a single outlier changes r. Skip the shared-variance diagram and the settings panel. (reading, 5–8 min)
Correlation: Meaning, Types, Examples & Coefficient — Simply Psychology — a plain-language page; read the parts on positive, negative, and zero correlation, on scatter plots, on the correlation coefficient from negative 1 to 1, and on correlation versus causation and the third-variable problem, and skip the rest. (reading, 5–8 min)

② Which total do I divide by? Marginal and conditional distributions

Chapter 4, Section “Read a two-way table two ways: marginal and conditional distributions” · Session 2, Segment 3, “Marginal and conditional distributions” · Remember: a marginal distribution divides by the grand total, and a conditional one divides by one row's or column's total.

Two Way Tables (Joint, Marginal, and Conditional) — Mimi's Math Channel — one table turned into joint, marginal, and conditional relative-frequency tables, step by step. Stop here before conditional probability; next week takes that up. (video, 7 min) (captions: automatic)
Two-Way Tables — Stat Trek — this time read the part on relative frequency for table rows and for table columns, which is a conditional distribution under another name. This builds on Week 2's work with summarizing a single categorical variable in a table. (reading, 5–8 min)

③ Does one cause the other? Association, causation, and lurking variables

Chapter 4, Section “Separate association from causation: lurking and confounding variables” · Session 2, Segment 4, “Association versus causation” · Remember: a strong association is a reason to ask about causes, and only a randomized experiment can settle them.

Correlation Doesn't Equal Causation: Crash Course Statistics #8 — CrashCourse — scatterplots, the correlation coefficient, and a lively case for why moving together is not cause and effect; its talk of regression and r-squared comes later in the course. (video, 12 min) (captions: creator)
Correlation and Causality – Don't Confuse Them (13-2) — Research By Design — why an experiment, where the researcher assigns the treatment, can rule out a third variable that an observational study cannot. (video, 11 min) (captions: creator)
Simpson's Paradox — ProfRobBob — one worked example of a lurking variable strong enough to reverse the direction of a comparison. (video, 5 min) (captions: creator)
Correlation and causation — Australian Bureau of Statistics — a national statistics office explains the difference, how r is read, and why a seasonal third factor can fake a link; use it while working through the Week-4 module overview. (reading, 6–9 min)

Optional one-stop reference (free online reference)

1.3.3.26 Scatter Plot — NIST/SEMATECH e-Handbook of Statistical Methods — a federal standards body's reference page on what a scatterplot can show: whether two variables are related, whether the pattern is straight or curved, and which points sit apart; read the purpose, the questions it answers, and the caution on causality, and skip the linked case study. (reading, 4–6 min)

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 reference above in the meantime.

Provenance: Built with The Course Maker · samples.thecoursemaker.com