Title: Week 2 Overview — Start Here
Course: Introduction to Statistics (18-Week Generic Edition)

Welcome to Week 2: Summarizing Data with Tables & Graphs

This is your home base for the week. Read it first, then work the checklist below from top to bottom. Everything you need is linked inside the module.

Last week you learned to ask where numbers come from — who was measured, how they were picked. This week is the first thing statisticians do with a trustworthy column of data: turn it into a table and a picture. A pile of raw numbers is unreadable; the right graph makes its story instant. And because pictures persuade instantly, the week ends with self-defense: the design tricks that make honest numbers lie — and how to spot every one of them.

The week's big question

"How do you turn a pile of numbers into a picture your eyes can read — and how do you spot the pictures built to fool you?"

By the end of the week you'll look at every chart — in the news, in an ad, in your own spreadsheet — with two questions ready: is this the right display for this variable? and is it drawn honestly?

By the end of this week, you can…

Use this as a checklist. If you can do all four out loud, you're ready for the quiz.

- [ ] Build a frequency table and a relative-frequency table — counts, then shares (count ÷ total) — and run the built-in check: shares sum to 1.
- [ ] Choose and read the right display — bar chart or pie for categories (pie only for parts of one whole), histogram / dot plot / stem plot for numbers. Remember the giveaway: bars apart = categories; bars touching = a number line.
- [ ] Describe a distribution's shape — symmetric, skewed right, skewed left, uniform, bimodal — arguing skew from the tail (the tail tells the tale), and flag informal outliers (investigate, don't delete).
- [ ] Spot a misleading graph — truncated axis, area tricks, missing labels, cherry-picked buckets — and state the fix (bars start at zero; if you zoom, label it loudly).

What to do this week, in order

The table below lists the week's items in working order, with what each is worth and when it's due.

# | Do this | Type | Due
1 | Read Chapter 2 — the module's primary reading | Chapter (ungraded prep) | Early in the week
2 | Skim the slides (Deck 2) and the Week 2 lecture outline; browse the Readings & Resources links that interest you | Prep (ungraded) | Alongside class
3 | Lecture Tutorial 2 — work through tables, displays, shape, and misleading graphs with your chatbot, then submit the share link + Completion Summary | Tutorial · graded (Lecture tutorials, 20% group) | End of Week 2
4 | Practice exercises — quick reps with the AI coach | Practice · ungraded | Before the quiz (recommended)
5 | Data Lab 2 — "Picture the Penguins: Honest Graphs & One Lying Axis" — table and graph the real penguin data, then build a deliberately misleading chart and fix it | Data lab · graded (Data labs, 15% group) | End of Week 2
6 | Quiz 2 — tables, displays, shape, misleading graphs | Quiz · graded (Quizzes, 15% group) · closed to AI | End of Week 2
7 | Discussion 2 — "The Zoomed-In Axis" — interrogate a real graph you've seen with your chatbot, post the AI summary + chat link, then reply to two classmates | Discussion · graded (Discussions, 15% group) | Initial post two days before week's end; replies by end of Week 2
8 | Assignment 2 — "Graphs Under Oath" — four AI-coached problems; submit the report (score on line 1) + chat link | Assignment · graded (Assignments, 25% group) | End of Week 2

Heads-up on the AI work: in this course the chatbot drafts, and you judge. Chatbots routinely flip a skew direction ("the tall bars are on the left, so it's skewed left" — wrong: skew follows the tail) and call histograms "bar charts." Catching the model is the point, and this week's lab and tutorial make you do exactly that.

Late policy reminder: 10% off per day late. If life happens, reach out to your instructor before the deadline — early is always easier.

How to succeed this week

- Lead with the variable, then pick the picture. Categorical or quantitative (Week 1's skill) decides everything: categories → bars or a legal pie; numbers → dot plot, stem plot, or histogram.
- Memorize the two giveaway hooks. "Bars apart = categories; bars touching = a number line" and "the tail tells the tale." Between them they answer half the quiz.
- Run the built-in checks. Counts sum to the total; relative frequencies sum to 1; pie slices sum to 100%. If a check fails, the table is confessing.
- Check the axis floor before you read any bar chart. Bars start at zero. If a graph zooms, it owes you a loud label.
- Do the lab's lying-axis exercise with feeling. Once you've drawn the 8× lie from a 1.2× truth yourself, no ad will ever pull it on you again.

You don't need any math beyond dividing a count by a total this week — just your eyes, a spreadsheet, and healthy suspicion. Come to the first session ready to be fooled by a graph, once, on purpose.