Course: Introduction to Statistics (MATH 11) · Silver Oak University (fictional sample) · Prof. Rivera
Module: Week 16 of 16 · Fall 2026 · in-person, two 75-minute sessions
Objectives covered: cumulative — Objectives 1–8 (Weeks 1–15): getting data; summarizing one variable; relating two variables; probability & random variables (binomial & normal); the normal & sampling distributions; confidence intervals; hypothesis tests; and simple linear regression with inference for the slope.

This file holds two pieces: (A) the Module 16 Overview page ("Start Here") and (B) the Welcome Announcement that drips out when the module opens. This is finals week — it works differently from a normal week. Dates below assume a Tuesday/Thursday session pattern with the Week 16 in-class review on Tue Dec 15; the Final window opens Mon Dec 14 and the exam is due Fri Dec 18, 11:59 p.m. (end of finals). Adjust the day-of-week and times to match your section.

(A) Module 16 Overview — Start Here

Welcome to Week 16: Final Review & Exam

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.

Heads-up: this is finals week, so it runs differently. There is no quiz, no discussion, and no assignment this week — the comprehensive Final replaces all of them. The week is built to get you ready: we spend our class session reviewing the whole course, you work through a three-part prep kit, and you sit the exam. The Final is cumulative over Weeks 1–15 (Objectives 1–8) — getting good data, describing one variable, relating two variables, reasoning about chance, working the normal and sampling distributions, building confidence intervals, running hypothesis tests, and fitting and testing a regression line. The midterm already covered the first half (Objectives 1–4), so the Final leans heaviest on the back half (Objectives 5–8) — but the early skills are the tools the later ones use, so they're fair game too.

The week's big question

"Across the whole course — get the data, describe it, relate it, model the chance, and use a sample to make a confident, tested claim about a population — can I do the one honest move each topic asks of me, and avoid the mistake that sinks it?"

By the end of the week you'll have walked the entire Objective 1–8 arc once more, found the exact spots where points get lost, and shown what you can do on the Final.

By the end of this week, you can…

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

• [ ] Get the data right (Obj 1) — population vs. sample, parameter vs. statistic, NOIR, a sampling method and its likely bias, observational vs. experiment.
• [ ] Describe one variable honestly (Obj 2) — read a histogram's shape, choose the right center (mean/median/mode) and spread (SD or IQR) under skew or outliers.
• [ ] Relate two variables carefully (Obj 3) — read a scatterplot and a correlation r, and explain why a strong link still isn't cause (hunt the lurking variable).
• [ ] Reason about chance (Obj 4) — the probability rules (complement, addition, conditional/multiplication, independence), expected value, and the binomial setting (BINS) with E(X) = n·p.
• [ ] Work the normal & sampling distributions (Obj 5) — turn a value into a z-score, read a normal probability, and use the Central Limit Theorem for the distribution of x̄ (standard error = σ/√n).
• [ ] Build & read a confidence interval (Obj 6) — estimate ± margin of error for a mean or proportion, and say correctly what "95% confident" means.
• [ ] Run a hypothesis test (Obj 7) — set up H₀ / Hₐ, and decide by comparing the p-value to α (p < α → reject; p ≥ α → fail to reject).
• [ ] Fit & test a line (Obj 8) — interpret a slope, intercept, and r², and decide whether the slope is significantly different from 0.

What's due this week, and what to do

Work these in order — each one gets you ready for the next. This is the finals-week list; there is no quiz, discussion, or assignment here — the Final stands in for all of them.

There is no Quiz 16, no Discussion 16, and no Assignment 16 this week — the Final stands in for all of them. The Study Guide, Exam-Prep Tutorial, and Practice Final are your prep kit; the Final is what's graded.

A note on the AI prep tutorial: the Exam-Prep Tutorial works like every weekly tutorial — the chatbot drafts and quizzes you, and you judge its work against what we covered. It will sometimes endorse a wrong "95% probability this interval" reading of a confidence interval, or call a non-significant p-value "proof of no effect"; catching that is part of being ready.

Late policy reminder: 10% off per day late — and the exam window is firm, and it's the end of the term, so don't let it sneak up. If life happens, reach out before the deadline; I'd much rather hear from you early than after.

How to succeed this week

• Review actively, not passively. Don't re-read notes — do the moves. Classify a variable, pick mean vs. median on a skewed set, read a correlation, compute a z-score, read a confidence interval, decide a test by p vs. α, interpret a slope. The Study Guide and Practice Final are built for exactly this.
• Study the eight honest moves, not a thousand facts. The Final is the eight objectives — one honest move each, and the mistake that sinks it. Learn those deeply and the exam stops feeling like "everything."
• Lean into the back half. The midterm already tested Objectives 1–4, so the Final weights 5–8 (the normal & sampling distributions, confidence intervals, hypothesis tests, regression) most heavily — but the early skills are tools the later ones use, so keep them sharp.
• Lead with the idea, then the number. Every topic this term was an idea first. On the exam, name the honest move before you reach for a formula: which summary tells the truth? is this a link or a cause? what does "95% confident" actually mean? does p beat α?
• Use the prep kit in order. Study Guide → Exam-Prep Tutorial → Practice Final. The tutorial finds your weak spots; the timed practice final tells you whether you've fixed them.

You've already done the hard part across fifteen weeks. This week is about pulling the whole course together and showing it. Come to class ready to review out loud — and bring your questions. See you Tuesday.

(B) Welcome Announcement — Module 16

Release setting: post on the module's start day (offset = 0 days), i.e., Mon Dec 14, 2026 (the day the Final window opens) — not before. If your platform won't preserve the scheduled post date on import, post this as a draft labeled "Release: Mon Dec 14."

Subject: Week 16 — Finals week: the whole course, one last time 🎓

Hi everyone,

Here we are — the last week. This one is different from the rest: it's finals week. There's no quiz, no discussion, and no assignment — the comprehensive Final takes their place. Everything this week is built to get you ready and then let you show what fifteen weeks built.

Here's the shape of it: our class session (Tue Dec 15) is a fast, complete review of the whole course — getting good data, describing one variable, relating two, reasoning about chance, the normal and sampling distributions, confidence intervals, hypothesis tests, and regression. The exam is cumulative over Objectives 1–8; because the midterm already covered the first half, the Final leans heaviest on the back half (Objectives 5–8) — but the early skills are the tools the later ones rest on, so keep them handy.

Your prep kit, in order: work the Study Guide first, then run the Exam-Prep Tutorial with an approved chatbot (Gemini, Claude, or ChatGPT) and submit the share link, then sit the Practice Final timed to find any soft spots.

The dates that matter:
1. Final — window opens Mon Dec 14, due Fri Dec 18, 11:59 p.m. (end of finals; 30% of your grade).
2. Exam-Prep Tutorial — submit your chat share link before the Final closes (Fri Dec 18).
3. In-class review — Tue Dec 15; come with questions.

A word as we close the term. When we started in Week 1, the whole promise was learning to interrogate a number before believing it — who was measured, how, and what was recorded. Everything since has been that same instinct, sharpened eight different ways: describe data honestly, relate it carefully, model the chance behind it, and use a sample to make a confident, tested claim about people you never fully counted. You can do all eight now. I've genuinely enjoyed watching you argue with budgeting apps, hunt lurking variables, and refuse to let a big sample bully you into trusting a biased one. This last exam isn't about cramming everything — it's about naming the eight honest moves and using them under one roof. You're ready.

Open the Start Here / Module Overview page first — it lays out the whole week in order with every due date. Thank you for a terrific semester.

You've got this. Come with questions Tuesday,
Prof. Rivera