Title: Week 2 — Readings & Resources · Summarizing Data with Tables & Graphs
Course: Introduction to Statistics (18-Week Generic Edition)

Course: Introduction to Statistics (18-week generic edition) Objective covered: Objective 2 — Summarize and display univariate data (this week: tables and graphs). Your primary reading is Chapter 2 (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 55–65 minutes if you do everything, far less if you pick one per group.

Order that matches the chapter and lecture: ① frequency & relative-frequency tables → ② the displays (bar, pie, histogram, dot, stem) → ③ shape & outliers → ④ misleading graphs.

A habit to start now: every time one of these resources shows you a graph, check the axis floor before you read the bars — bars start at zero; if it zooms, it should say so loudly.

① Frequency & Relative-Frequency Tables

Maps to Chapter 2, Section 1 and Lecture Segment 2. The hook to carry: frequency counts it; relative frequency shares it — and shares must sum to 1.

Reading — Frequency Distribution: Tables, Types & Examples (Scribbr) 🔗 https://www.scribbr.com/statistics/frequency-distributions/ Why it's assigned: the cleanest walk from raw values to grouped and relative-frequency tables — including the class-interval move for quantitative data — with worked tables that mirror ours. ⏱ ~7 min

② The Displays: Bar, Pie, Histogram, Dot, Stem

Maps to Chapter 2, Sections 2–3 and Lecture Segments 3 & 5. The giveaway to remember: bars apart = categories; bars touching = a number line.

Reading — Histograms, Frequency Polygons, and Time Series Graphs (OpenStax Introductory Statistics 2e, Section 2.2) 🔗 https://openstax.org/books/introductory-statistics-2e/pages/2-2-histograms-frequency-polygons-and-time-series-graphs Why it's assigned: a careful, step-by-step build of a histogram — choosing classes, handling boundary values, drawing the touching bars — from the free text this course uses as its one-stop reference. ⏱ ~10 min

Video — Charts Are Like Pasta - Data Visualization Part 1: Crash Course Statistics #5 (video, ~12 min, captioned) 🔗 https://www.youtube.com/watch?v=hEWY6kkBdpo Why it earns the click: a lively tour of frequency tables, bar charts, pie charts, and when each earns its place — the categorical half of the week in one sitting.

Video — StatQuest: Histograms, Clearly Explained (video, ~4 min, captioned) 🔗 https://www.youtube.com/watch?v=qBigTkBLU6g Why it earns the click: the fastest clear explanation of what a histogram actually is — values stacked into bins — plus a first taste of why bin width matters.

③ Shape & Outliers

Maps to Chapter 2, Section 4 and Lecture Segment 6. The trap-killer: skew is named for the tail, never the peak — the tail tells the tale.

Reading — Skewness: Definition, Examples & Formula (Scribbr) 🔗 https://www.scribbr.com/statistics/skewness/ Why it's assigned: plain-language pictures of right- and left-skewed distributions with the tail direction made explicit — read only the definition and examples sections; the formula belongs to a later course. ⏱ ~6 min

Video — Plots, Outliers, and Justin Timberlake: Data Visualization Part 2: Crash Course Statistics #6 (video, ~12 min, captioned) 🔗 https://www.youtube.com/watch?v=HMkllhBI91Y Why it earns the click: histograms, dot plots, stem plots, and shape vocabulary in action — plus a first look at how outliers jump out of a plot.

④ Misleading Graphs

Maps to Chapter 2, Section 5 and Lecture Segment 7. The pocket rule: bars start at zero; if you zoom, label it loudly.

Reading — Misleading Graphs: Real Life Examples (Statistics How To) 🔗 https://www.statisticshowto.com/probability-and-statistics/descriptive-statistics/misleading-graphs/ Why it's assigned: a gallery of real published graphs gone wrong — truncated axes, cherry-picked scales, missing labels — each one annotated with what makes it deceptive. ⏱ ~8 min

Video — How to spot a misleading graph - Lea Gaslowitz (TED-Ed) (video, ~4 min, captioned) 🔗 https://www.youtube.com/watch?v=E91bGT9BjYk Why it earns the click: four minutes that permanently change how you look at charts in ads and news feeds — the exact tricks from Segment 7, animated.

Optional one-stop reference (free online text)

If you'd like one optional reference to skim all term, OpenStax Introductory Statistics 2e keeps its full text free to read online. Chapter 2 (Descriptive Statistics) covers this week end to end — stem plots, bar graphs, histograms, and distribution shape (its later sections on center and spread preview Week 3). 🔗 https://openstax.org/books/introductory-statistics-2e/pages/2-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 (≈20 min total)

In a hurry? You've read Chapter 2 — then do exactly these three and you'll be ready for the quiz: 1. Watch StatQuest: Histograms, Clearly Explained (group ②). 2. Read Skewness — Scribbr (group ③, definition and examples only). 3. Watch How to spot a misleading graph — TED-Ed (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.