Introduction to Statistics
Built with The Course Maker · samples.thecoursemaker.com
This is a complete course: fifteen instructional weeks and a finals week, every student component, built with The Course Maker and ready to import. Weeks are relative (Week 1 to Week 16); you set the dates when you import it. Browse the whole thing below. Click any piece to read it in full, then click back to return here.
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Download this course
This course is free, complete, and evergreen, and it is yours to keep; free downloads run through June 30, 2028. Students pay $0 for course materials: the readings and videos are links to freely available resources.
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What the paid build adds
This sample is complete and yours to keep. A paid build is the same course made yours in the ways a download cannot be:
- Every date set to your term and your breaks, and re-paced to a second campus calendar at no extra charge.
- Your syllabus, your policies, and your outline of record in the build, with an alignment report that shows which component addresses each objective and SLO, and a Universal design for learning section drawn from the course's own data.
- Your name on every page as the course's creator.
- Kept current every term: fresh assessment variants, every link re-checked and the dead ones replaced, and the subject brought up to date where it has moved.
- The full instructor pack: answer keys and worked solutions, the alignment report, and the UDL map.
- A record for your college's file: the design attestation, a disclosure statement for every edition, and what changed at the end of each term.
Included in every course
ChapterRequired
Required in every course. Every module presents the same content in three ways — a chapter to read, a slide deck with speaker notes, and captioned videos — so each student can choose how to take it in. The course is designed to reflect UDL's multiple means of representation, and the alignment report describes this to your curriculum committee.
Slide deck with speaker notesRequired
Required in every course. Every module presents the same content in three ways — a slide deck with speaker notes, a chapter to read, and captioned videos — so each student can choose how to take it in. The course is designed to reflect UDL's multiple means of representation, and the alignment report describes this to your curriculum committee.
Captioned video linksRequired
Required in every course. Every module presents the same content in three ways — captioned videos, a chapter to read, and a slide deck with speaker notes — so each student can choose how to take it in. The course is designed to reflect UDL's multiple means of representation, and the alignment report describes this to your curriculum committee. We link videos only when a caption track exists (from the creator or generated automatically), and we never host them.
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Policies, schedule, and grading
Assignment groups & weights
Configured in the export — the gradebook is set the moment the course is imported.
| Assignment group | Weight | Notes |
|---|---|---|
| Tutorials & Practice | 10% | |
| Quizzes | 10% | |
| Workshops | 20% | |
| Assignments | 25% | |
| Discussions | 15% | |
| Exams | 20% | |
| Total | 100% | Letter Standard |
What students will be able to do
Distinguish populations from samples and parameters from statistics, classify variables by type and level of measurement, and identify appropriate sampling methods and study designs, including what each can and cannot conclude.
Summarize and display a single quantitative or categorical variable with appropriate tables and graphs, and describe its distribution's shape, center, and spread with the right numerical summaries.
Describe the relationship between two variables using scatterplots, the correlation coefficient, and two-way tables, and explain why association alone does not establish causation.
Apply the basic rules of probability, including conditional probability and independence, and compute and interpret the expected value and standard deviation of a discrete random variable, including binomial settings.
Use the normal model and sampling distributions, including the Central Limit Theorem, to reason about variability in individual values and in sample statistics.
Construct and correctly interpret confidence intervals for a population mean and a population proportion, including the meaning of the confidence level and the margin of error.
Carry out and correctly interpret hypothesis tests for means and proportions, including one- and two-sample settings, p-values, significance levels, and Type I and Type II errors.
Fit and interpret a simple linear regression model, assess its fit with r-squared and residuals, and carry out inference for the slope.
Reason quantitatively to draw evidence-based conclusions from data, stating what the evidence does and does not support.
Communicate statistical results clearly and honestly to a non-technical audience, in plain language and with the appropriate caveats.
How this course is built — read this first
Every teaching module carries the same set, in the order a student meets it: a Start Here overview, the chapter (the primary reading), the lecture (titled “Lecture: <module title>”), the slide deck with speaker notes, readings & resources (links only), the lecture tutorial and practice exercises worked with an AI tutor, the quiz, the discussion and the assignment, and the weekly Data Lab. Exams: the midterm in Week 8 and the final in Week 16, a finals week with no module.
The discussion and the assignment each ship in both formats: the adaptive version is the published, graded item, and its traditional alternative (no AI) is a separate item that ships unpublished beside it. An instructor who prefers the traditional format publishes it and unpublishes or deletes the adaptive one.
What you see here is every student page. The instructor pack (answer keys and worked solutions, rubric calibration, image prompts, audio-brief scripts, and the alignment report) ships only in the download, never on this site.
Discussions & assignments: both formats in every module
Same learning objective either way; what changes is how the work happens.
Work it through with an approved chatbot
The student does the work in a guided conversation with an approved chatbot, using a copy-paste prompt the course provides. For a discussion, the AI is a Socratic partner that challenges their thinking and never writes the post. For an assignment, the AI is a coach: it asks questions, checks the reasoning shown, and waits for the student to revise. The student submits the chat link with the required summary or report.
The familiar way, one click away
The student does the work themselves and submits it, and the instructor grades it against the rubric. No AI required. It ships unpublished, opening with a one-line note to the instructor; publish it instead of the adaptive version if you prefer.
Every week, every component
Each week is a heading; every component under it links to the full student page. Week 8 is a teaching module that also carries the midterm bundle; Week 16 is the finals week, with the final exam bundle and no module.
Foundations & Types of Data
Summarizing Data with Tables & Graphs
Numerical Summaries: Center & Spread
Exploring Relationships Between Two Variables
Probability Foundations
Random Variables
Binomial & Normal Models
Midterm Review & Exam
The midterm exam bundle