Course: Introduction to Statistics (MATH 11) · Silver Oak University (fictional sample) · Prof. Rivera
Module: Week 2 of 16 · Fall 2026 · in-person, two 75-minute sessions
Objective covered: Objective 2 — Summarize and display univariate data — its shape, center, and spread (this week: the display half — tables and pictures of one variable).

This file holds two pieces: (A) the Module 2 Overview page ("Start Here") and (B) the Welcome Announcement that drips out when the module opens. Dates below assume a Tuesday/Thursday session pattern with Week 2 meeting Tue Sep 8 and Thu Sep 10, and end-of-week work due Sunday Sep 13, 11:59 p.m. Adjust the day-of-week and times to match your section.

(A) Module 2 Overview — Start Here

Welcome to Week 2: Summarizing Data

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 we learned how to get trustworthy data — who was measured and how they were picked. This week we do the very first thing anyone does with data: turn a pile of raw numbers into a picture a human can actually read. Thirty loose numbers tell you nothing; the same thirty numbers, drawn as a histogram, show you a typical value, how spread out things are, and whether anything is weird — all in one glance. The catch: the same picture can quietly lie, especially when one outlier is hiding in it. Learning to build these pictures and to refuse to be fooled by them is the whole week.

The week's big question

"How do you turn a pile of raw numbers into a single picture that tells the truth about them — and how can that same picture lie?"

By Friday you'll be able to take any list of numbers and, in two moves — a table, then a histogram — see its shape, its center, its spread, and the outliers that would otherwise fool you.

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 week's graded work.

• [ ] Build a frequency and relative-frequency table from raw data — choosing sensible classes, and remembering that frequency = how many while relative frequency = what share (count ÷ total, and the shares add to 1).
• [ ] Build and read a histogram — know that the bars touch because the axis is a number line, that bar height = the count in that class, and how a histogram differs from a bar chart (which is for categories, with gaps between bars).
• [ ] Name a distribution's shape — symmetric, skewed left, skewed right, uniform, or bimodal — remembering that skew is named for the tail, not the lump, and read center and spread right off the picture.
• [ ] Spot an outlier and explain its effect — how one far-out value drags the mean (but barely moves the median) and can flatten a whole histogram.

What's due this week, and when

Work these in order — each one gets you ready for the next.

Heads-up: this week's graded set is Quiz 2, Discussion 2, and Assignment 2, plus the weekly Lecture Tutorial. The adaptive assignment lets you keep improving your score by learning, so start early enough to enjoy the re-tries.

Heads-up on the AI work: you'll use a chatbot to draft, and then you judge its work against what we cover in class. Chatbots routinely miss these — they'll call an outlier-laden list "symmetric," or report a mean that an outlier has dragged away from the real center. Catching the model is the point.

Late policy reminder: 10% off per day late. If life happens, reach out before the deadline — I'd much rather hear from you early.

How to succeed this week

• Lead with the picture, not the formula. Everything this week is about seeing a variable. We don't compute exact center and spread yet (that's Week 3) — we read them off a histogram. If you can sketch the shape, you understand the data.
• Memorize three tiny hooks. "Frequency = how many, relative frequency = what share." "Bars touch → histogram (numbers); bars apart → bar chart (categories)." And the big one: "Skew is named for the tail, not the lump."
• Describe any distribution with one checklist: Shape · Center · Spread · Outliers (S-C-S-O), always in that order, always in plain language first.
• Respect the outlier. One far-out value can move the mean a lot while the median barely moves, and it can flatten a whole graph. Notice it, report its effect — don't silently delete it.
• Treat the chatbot as a smart intern, not an oracle. It drafts a shape or a "typical value"; you check it against the picture. That habit is the whole semester in miniature — and it's literally how Assignment 2 is scored.

You don't need anything from a textbook this week — just the data we work with in class and a spreadsheet to draw it. Come ready to argue about whether a neighborhood's "average" home price means anything. See you Tuesday.

(B) Welcome Announcement — Module 2

Release setting: post on the module's start day (offset = 0 days), i.e., Tue Sep 8, 2026 — not before. If your platform won't preserve the scheduled date on import, post this as a draft labeled "Release: Tue Sep 8."

Subject: Week 2 — turning a pile of numbers into a picture 📊

Hi everyone,

Quick experiment: imagine I put thirty random commute times on the screen and gave you five seconds to tell me the typical commute and whether anyone's is unusual. You couldn't — nobody can read thirty loose numbers. That's the whole problem Week 2 solves.

This week — Summarizing Data — we tackle the big question: How do you turn a pile of raw numbers into a single picture that tells the truth about them — and how can that same picture lie? By Friday you'll take any list of numbers and, in two moves — a table, then a histogram — see its shape, center, spread, and any outliers trying to fool you.

The one thing not to miss:
1. Assignment 2 (adaptive) — work four problems with an approved chatbot (Gemini, Claude, or ChatGPT); it grades you against a rubric, teaches you the fixes, and lets you retry for a higher score. Submit the AI's self-scored report plus your chat link. Worth 100 points (Assignments group). Due Sun Sep 13.
2. Quiz 2, Discussion 2, Lecture Tutorial 2, and the practice set also close Sun Sep 13 — the tutorial and practice are the on-ramp; do them first.

A callback to last week: we learned that a number is only as good as the sample behind it. Now we take good data and give it a shape we can see — and learn to spot the one outlier that can quietly distort the whole story. Remember the week's mantra: skew is named for the tail, not the lump.

Open the Start Here / Module Overview page first — it lays out everything in order with due dates. Bring your curiosity (and maybe an opinion about whether a neighborhood's "average" home price means anything) to class on Tuesday.

See you soon,
Prof. Rivera