Introduction to Statistics

Fictional sample for demonstration. Silver Oak University and Prof. Rivera are fictional, used to showcase thecoursemaker.com. No real institution, course, or person is implied or endorsed.

Course Description

Introduction to Statistics is a one-semester, lower-division survey of statistical reasoning for students across every major. We move along the natural arc of the subject — describe → relate → quantify uncertainty → infer → model — and at each step we lead with the plain-language idea before the notation. You will learn to summarize and display data, reason about chance and variability, build and interpret confidence intervals and hypothesis tests, and fit a simple regression model.

The emphasis throughout is on understanding and interpretation, not memorizing formulas. We use spreadsheets (Google Sheets or Excel) for computation and charts, do hand computation on small cases to build intuition, and adopt an "interpret-the-output" stance for the heavier machinery. Examples are drawn from your own fields of study, and a weekly AI-tutor tutorial gives you a low-stakes place to practice and check your thinking. No prior statistics experience is assumed.

Learning Objectives

By the end of the course, you will be able to:

1. Distinguish populations from samples and identify appropriate sampling and study designs.
2. Summarize and display univariate data, describing shape, center, and spread.
3. Describe relationships between two variables using scatterplots, correlation, and two-way tables.
4. Apply basic probability rules, including conditional probability, and work with random variables.
5. Use normal and sampling distributions to reason about variability.
6. Construct and interpret confidence intervals for means and proportions.
7. Conduct and interpret hypothesis tests for means and proportions.
8. Fit and interpret a simple linear regression model, including inference for the slope.

Student Learning Outcomes (SLOs)

• SLO A — Quantitative reasoning. Reason quantitatively to draw evidence-based conclusions from data.
• SLO B — Communication. Communicate statistical results clearly to a non-technical audience.

Required Materials

There is no required textbook, and you will pay nothing for course materials. Readings are delivered as links to external resources posted in each weekly module. The reading load is intentionally light and is meant to support, not replace, the in-class work.

You will need:

• A device with a web browser and internet access.
• A spreadsheet tool — Google Sheets (free) or Microsoft Excel — for computation and charts.
• Access to one approved AI chatbot for the weekly Lecture Tutorials (see the AI-Use Policy below).

Grading

Your course grade is the weighted total of the groups below. Weights sum to 100%.

Attendance is tracked at every session but is not weighted (see the Attendance Policy).

Letter-Grade Scale

Late & Make-Up Policy

• Late penalty: 10% per day. Submitted work loses 10 percentage points of its earned score for each day (or part of a day) it is late.
• Quizzes, the Midterm, and the Final are time-bound. Make-ups are arranged only for documented emergencies — contact Prof. Rivera as early as possible, ideally before the due date.
• Practice exercises are ungraded and exist for your benefit; the late penalty does not apply to them.
• If something serious is getting in the way of your work, reach out early. It is almost always easier to arrange support before a deadline than to repair a grade after it.

AI-Use Policy

This course requires you to use AI as a learning partner, and it draws a clear line for everything else. Read this section carefully.

Approved chatbots

You must use one of these three approved AI chatbots:

• Gemini
• Claude
• ChatGPT

The free tier of any of these is sufficient. You may pick whichever you prefer.

AI in this course (adaptive-learning activities)

This course uses an approved chatbot — Gemini, Claude, or ChatGPT (free tier is fine) — as part of several activities. Your Lecture Tutorials, Discussions, and Assignments are adaptive-learning activities you complete with the chatbot:

• Weekly Lecture Tutorials — work through the week's ideas in conversation, then submit the conversation share link and your Completion Summary.
• Discussions — think a question through in a real-time dialogue with the chatbot, then post the AI-generated summary plus your chat share link to the discussion board (and reply to peers).
• Assignments — solve AI-posed problems with a chatbot coach that grades and teaches you as you go, then submit the coach's self-scored report (the line beginning STUDENT'S SCORE:) plus your chat share link.

For all three, the share link is part of your submission — treat the conversation as your work, keep it on-topic, and do your own thinking.

Permitted vs. not permitted

• AI may be used on your coursework — the Lecture Tutorials, Discussions, Assignments, and the ungraded Practice Exercises. (For the adaptive activities above, working with the chatbot is the activity.)
• AI may not be used on the Quizzes, the Midterm, or the Final — these are closed to AI and must be entirely your own work, unless an item explicitly says otherwise.

Disclosure

The adaptive activities (tutorials, discussions, assignments) need no separate disclosure — the share link already documents your AI use. If you use an AI tool to help you think about any other graded work, add a one-line note stating which tool you used and how.

Alignment with academic integrity

Using AI as described here is encouraged and fully consistent with the integrity standard below. The violations are fabricating or doctoring a chat you submit, and using AI on the closed assessments (Quizzes, Midterm, Final). When in doubt, ask before you submit.

Attendance Policy

This is an in-person course that meets twice a week, and the in-class work — worked examples, think-pair-share, the technology-and-AI-critique moments — is where much of the learning happens.

• Attendance is tracked at every session. It is not part of your weighted grade, but a strong attendance record is expected and consistent absence will show in your performance.
• Arrive on time, stay for the full session, and engage professionally with your classmates and instructor.
• If you must miss a session, notify Prof. Rivera in advance when possible and review the module materials to catch up. You remain responsible for any content, announcements, and due dates from a missed class.

Academic Integrity

You are expected to do your own work and to represent it honestly. Cheating, plagiarism, unauthorized collaboration, and submitting another's work — human or AI — as your own are violations of academic integrity and will be handled according to university policy, which may include a failing grade on the work or in the course. Collaboration is welcome where an assignment invites it; when in doubt about what is allowed, ask first. Holding to this standard is what makes your grade — and your degree — mean something.

Accessibility: Silver Oak University is committed to equal access. Students who need accommodations should contact the campus disability services office to arrange them; notify Prof. Rivera early in the term so supports can be in place. (Placeholder — institutions should insert their official accessibility, Title IX, and integrity statements here.)

Course Schedule — Fall 2026 (16 Weeks)

Term runs Aug 31 – Dec 18. Campus holidays: Labor Day (Sep 7), Veterans Day (Nov 11), Thanksgiving (Nov 26–27). Week 16 is reserved for finals. Dates are the Monday of each week.

Practice exercises and a Lecture Tutorial are part of every week's module; the table lists the graded touchpoints. The schedule may be adjusted with advance notice; changes will be announced in the course.