STAT 35000: Introduction to Statistics — Summer Session 2026

Intensive Asynchronous Online | June 15 – August 7, 2026 (2nd 8 Weeks)

Welcome to the Summer offering of STAT 35000. This is a compressed format—a full 16-week course delivered in 8 weeks. Statistics isn’t just about crunching numbers; it’s about understanding uncertainty and making well-informed decisions based on evidence.

Critical Notice

This Summer session compresses an entire semester into eight weeks. Plan on 15–20 focused hours per week, with some weeks heavier than others. The compressed calendar leaves little room for catching up, so daily engagement is essential.

Why This Course? Why Now?

We live in an era dominated by data. Every decision—from public health policies to business strategies—depends on accurate statistical reasoning.

• Critical decision-making: Statistics is the backbone of research and industry. Incorrect interpretation can have serious real-world consequences.

• Understanding uncertainty: Life is unpredictable. Statistical methods help you quantify, understand, and communicate uncertainty clearly.

• The backbone of AI and machine learning: All modern AI models rely heavily on probability theory and statistical modeling—critical skills for the future workforce.

Summer Session Format

What makes this different:

• 8 weeks covering 13 chapters of material

• Weekly cycle: Absorb → Apply → Assess → Review (~15–20 hours/week)

• 3 proctored exams: Exam 1 (July 7), Exam 2 (July 21), Final Exam (August 5)

• No makeup opportunities: The accelerated pace leaves no buffer for catching up

View the complete schedule: Summer Session Schedule

Weekly Time Commitment:

📹 Lecture Videos (Absorb)	6–8 hours
✏️ Homework & Computer Assignments (Apply)	6–8 hours
✅ Weekly Quiz (Assess)	20 mins
🔁 Review	~2 hours
⏰ TOTAL	~15–20 hrs/week

Video Learning Platform

Open the STAT 350 Video Learning Platform

What it provides

• All lecture videos organized by day with segment counts and total durations.

• An interactive timeline for “click to jump” navigation by topic.

• Theater Mode for focused viewing; Print to generate a printable outline.

• Micro-lecture candidates visually highlighted for quick review.

• Search/filter to locate topics across all 84 videos.

Notes

• Best viewed on a desktop browser.

• If a video fails to load, refresh the page or use the direct YouTube link.

Interactive Learning Tools

In addition to the webbook and video platform, you have access to browser-based tools designed to support your learning in this compressed format:

Interactive Simulators	Browser-based visualizations for the Central Limit Theorem, confidence intervals, and power analysis. Experiment with sampling distributions and see concepts in action—no R installation required. Open CLT Simulator
Statistical Procedures Tool	An interactive decision flowchart for choosing the correct statistical method based on your research question and data type. Use this as a study aid when reviewing for exams. Open Procedures Tool
AI Course Assistant (Beta)	A chatbot trained on all STAT 350 course materials that can answer conceptual questions, point you to relevant webbook sections, and help clarify confusing topics. Available 24/7. Open AI Assistant

About the AI Course Assistant

This tool is currently in beta testing. You may occasionally encounter incomplete answers, unexpected behavior, or temporary unavailability. Use it as you would a tutor—for explanation and clarification—not as a replacement for doing the work yourself.

Report any issues (incorrect information, broken features, confusing responses) to Dr. Reese via email so the tool can be improved.

Remember: The AI assistant will not be available during exams. Students who rely on AI as a crutch rather than a learning tool consistently perform poorly on proctored assessments.

8-Week Course Roadmap

Week	Topic & Focus	Chapters
1	Foundations: Data types, descriptive statistics, R basics	Ch 1–3
2	Probability & Discrete Distributions: Rules, conditional probability, Bayes’ Theorem, Binomial, Poisson	Ch 4–5
3	Continuous Distributions & Sampling: Normal, exponential, sampling distributions, CLT	Ch 6–7
4	Design, Single-Sample Inference, Exam 1: Experimental design, confidence intervals, intro to hypothesis testing	Ch 8–10
5	Hypothesis Testing & Two-Sample Methods: Tests for μ, comparing two populations	Ch 10–11
6	ANOVA, Exam 2: Comparing multiple groups simultaneously	Ch 12
7	Regression: Modeling relationships, correlation, diagnostics, inference	Ch 13
8	Review and Final Exam	Ch 1–13

Your Weekly Learning Cycle

1. Absorb (≈6–8 hours)

• Complete assigned reading in the free digital webbook

• Watch segmented lecture videos using the Video Learning Platform

• Use search bar and timeline to target challenging topics

2. Apply (≈6–8 hours)

• Work through Edfinity homework on scratch paper first

• You have 10 attempts with instant feedback—use them to learn, not guess

• Complete computer assignments using R

• Record steps that slow you down for exam review

3. Assess (≈20 minutes)

• Take the timed weekly quiz in Brightspace

• Quizzes focus on conceptual understanding, mirroring exam questions

4. Review (≈2 hours)

• Check posted solutions

• Revisit video segments on challenging topics

• Post or answer questions on the peer discussion board

Success Tips

• Start early: The course opens before June 15—begin working ahead

• Log in daily: Check deadlines and announcements every day

• Use the Video Platform strategically: Search for specific topics, use micro-lectures for quick review

• Work ahead during lighter weeks to build a buffer for heavier ones

• Leverage peer support: Your classmates are your best resource for immediate help

• Don’t rely heavily on AI: Students who use AI as a crutch consistently perform poorly on exams

• Plan for limited instructor response: Email replies may take 24–48 hours; plan questions accordingly

Exam Requirements: Respondus LockDown Browser

All three exams—Exam 1 (July 7), Exam 2 (July 21), and the Final Exam (August 5)—are proctored online using Respondus LockDown Browser with webcam monitoring. This system ensures exam integrity by restricting your computer to only the exam while monitoring you through your webcam.

What is Respondus LockDown Browser?

Respondus LockDown Browser is a custom browser that locks down the testing environment within Brightspace. When you launch the exam:

• Your computer is locked to the exam application only—you cannot access other programs, websites, or files

• Your webcam records you throughout the entire exam session

• Your screen is recorded to detect any unauthorized activity

• You cannot print, copy, access other applications, or use virtual machines

• The software includes a built-in scientific calculator for your use

WATCH THIS VIDEO BEFORE STARTING THE COURSE

You must watch this setup video to ensure you meet all exam requirements:

Failure to prepare your testing environment properly may prevent you from taking the exam, and there are no makeup opportunities in this summer session.

Required Equipment:

Computer	Desktop or laptop (Windows or macOS). Tablets and Chromebooks are NOT supported
Webcam	Working webcam that shows both your face and writing area. Most laptop webcams are not wide enough—you may need an external USB webcam
Microphone	Working microphone for audio recording
Internet	Stable connection of at least 2 Mbps (test at speedtest.net)
ID	Government-issued photo ID for identity verification before starting

Testing Environment Requirements:

• Private, quiet room with no other people present

• Good lighting so your face is clearly visible throughout the exam

• Clear desk with only allowed materials (see below)

• No headphones or earbuds during the exam

• No dual monitors—disconnect or disable second screens

• Webcam positioned to show your face and hands/desk area at all times

Allowed Materials During Exams:

• Handwritten or printed notes (your own crib sheets)

• Basic calculator (or use the built-in calculator in LockDown Browser)

• Scratch paper and pencil/pen for calculations

• Statistical tables if needed (URLs will be provided in exam and allowed by browser)

NOT Allowed:

• Textbooks or course materials beyond your personal notes

• Electronic devices (phones, tablets, smartwatches, etc.)

• Other people in the room

• Browsing other websites (system prevents this)

• Communication with anyone during the exam

How Exams Work:

1. 24-hour window: Each exam is available for a 24-hour period to accommodate different schedules

2. Fixed time limit: Once you begin, you have a set time (80 min for Exam 1 and Exam 2, 140 min for the Final)

3. One attempt only: You cannot pause, exit, or restart the exam

4. Identity verification: Show your ID to the webcam at the start

5. Continuous monitoring: Your webcam and screen record throughout

6. Automatic submission: Exam auto-submits when time expires

Technical Requirements:

• Download and install Respondus LockDown Browser well before exam day. A TA will contact you if your setup is in violation.

• Run the built-in system check to verify your setup

• Close all other programs before launching the exam

• Ensure your computer is plugged in (don’t rely on battery)

• Unplug additional monitors.

• Clear browser cache if you experience technical issues

Practice Run - DO THIS BEFORE EXAM 1:

Brightspace will have a practice quiz using LockDown Browser available before the first exam. Complete this practice session to:

• Test your webcam and microphone

• Verify your internet connection is sufficient

• Practice the identity verification process

• Familiarize yourself with the exam interface

• Identify any technical issues before exam day

Critical Exam Preparation Timeline

At least 4 days before Exam 1 (by July 3):

1. Download and install Respondus LockDown Browser

2. Complete the practice quiz to test your full setup

3. Verify your webcam shows both face and desk area

4. Ensure you have a Purdue ID available

5. Prepare your testing environment (clear desk, good lighting, quiet room)

6. If you encounter any issues, contact instructor immediately

What Happens if I Have Technical Issues?

• Before the exam: Email the TA immediately to discuss next steps.

• During the exam: If you experience a technical failure, document it immediately and contact the instructor as soon as possible

• No makeup exams: Technical issues on your end do not guarantee a makeup opportunity in this compressed format

Academic Integrity:

The webcam monitoring system flags suspicious behaviors including:

• Looking away from the screen frequently

• Talking or moving your lips (suggesting communication)

• Leaving the camera view

• Multiple people detected in the room

• Use of unauthorized materials

• Unusual background noise

All recordings are reviewed by TAs not just those flagged by Respondous. Academic misconduct will result in serious consequences including failing the course and referral to the Office of Student Rights and Responsibilities.

Learning Resources & Technology

Digital Textbook (FREE)	Complete course materials, videos, data sets, slides, searchable formulas, and R code at STAT 350 Website
Video Learning Platform	84 indexed lectures (~29 hours) with micro-segments, interactive timeline, and search functionality
Brightspace	Quizzes, exams, announcements, peer discussions, grades
Edfinity ($40)	All homework and computer assignments with automated feedback
R/RStudio	Required for computer assignments. Local Install Guide or Scholar Access
Respondus LockDown Browser	Required for proctored exams with webcam monitoring. Download from Brightspace link.

Assessment & Grading

Category	Weight
Weekly Quizzes (lowest 2 dropped)	10%
Homework & Computer Assignments (lowest of each dropped)	26%
Exam 1 (July 7)	18%
Exam 2 (July 21)	18%
Final Exam (August 5)	28%
Course Evaluation (Bonus)	+1%

Communication & Support

Instructor: Dr. Timothy Reese | reese18@purdue.edu | MATH 210

• Email: Use subject line “STAT 35000: <topic>”. Expect 24–48 hour response time, potentially longer on weekends/holidays

• Peer Discussion Board: “Q&A for Peers” on Brightspace—your quickest help resource. Instructor will not monitor regularly.

• Office Hours: Held online via WebEx by appointment. Email the TAs to request a time.

Peer Support is Essential

In this accelerated format, your classmates are critical to your success. Build connections early, participate actively in discussions, and help each other. Students who engage with peers perform significantly better.

Course Evaluation & Feedback

End-of-Course Evaluation (Bonus Credit)

The anonymous online course evaluation opens Monday, July 27 and closes Friday, August 7, 2026. You’ll receive an email at your Purdue address with a direct link. Your feedback helps improve this course and future Summer Session offerings—results are only released after final grades are submitted.

Participation Incentive

If at least 80% of enrolled students complete the evaluation by August 7, every student receives 1% extra credit added to their final course percentage.

Feedback on Learning Tools

Many of the tools you are using this semester—the interactive webbook, Video Explorer, browser-based simulators, Statistical Procedures Tool, and AI Course Assistant—were developed specifically to support students in this intensive format. Your experience using these tools is valuable for improving them.

As you work through the course, consider:

• Which tools did you find most helpful for learning?

• Were any tools confusing or difficult to navigate?

• What features would make the Video Explorer or webbook more useful?

• Did the AI Course Assistant provide accurate, helpful responses?

• What’s missing that would have supported your success?

You can share this feedback through:

• The course evaluation (include comments about specific tools)

• Email to Dr. Reese at any point during or after the course

• The peer discussion board if you have suggestions others might benefit from

Your input directly shapes how these tools evolve for future students.

Getting Started Checklist

✔	Action Item
☐	Review the syllabus and mark all exam dates and weekly deadlines
☐	Explore the Summer Session Schedule
☐	Set up R/RStudio using either local install or Scholar access
☐	Download and install Respondus LockDown Browser
☐	Complete the practice quiz to test your exam setup
☐	Watch the Respondus Setup Video
☐	Verify you have proper webcam and environment that shows face, hands, AND desk area
☐	Prepare your testing environment (clear desk, good lighting, quiet private room)
☐	Bookmark course sites: Brightspace, Edfinity, Course Website, Video Learning Platform
☐	Complete the syllabus quiz on Brightspace
☐	Introduce yourself on the peer discussion board
☐	Begin Week 1 materials as soon as available

AI Usage Policy

AI tools (ChatGPT, Claude, etc.) may be used according to these guidelines:

Permitted:

• Checking reasoning or getting hints after attempting problems yourself

• Understanding concepts after reading textbook/watching lectures

• R coding assistance (debugging, syntax) after attempting code yourself

Prohibited:

• Generating complete solutions to homework or assignments

• Submitting AI-generated content as your own

• Using AI during exams (this is academic misconduct)

Critical Warning

You must be able to explain any work you submit. Homework is practice for exams where AI is unavailable. Over-reliance on AI will result in poor exam performance and potential academic misconduct charges.

Get comfortable, stay focused, and let’s make these 8 weeks count!

Boiler Up!

Chapters

• 1. Introduction to Statistics
  • 1.1. What Is Statistics?
  • 1.2. Probability & Statistical Inference: How Are They Associated?
• 2. Graphical Summaries
  • 2.1. Data Set Structure and Variable Types
  • 2.2. Tools for Categorical (Qualitative) Data
  • 2.3. Tools for Numerical (Quantitative) Data
  • 2.4. Exploring Quantitative Distributions: Modality, Skewness & Outliers
• 3. Numerical Summaries
  • 3.1. Introduction to Numerical Summaries: Notation and Terminology
  • 3.2. Measures of Central Tendency
  • 3.3. Measures of Variability - Range, Variance, and Standard Deviation
  • 3.4. Measures of Variability - Interquartile Range and Five-Number Summary
  • 3.5. Choosing the Right Measure & Comparing Measures Across Data Sets
• 4. Probability
  • 4.1. Basic Set Theory
  • 4.2. Probability
  • 4.3. Conditional Probability
  • 4.4. Law of Total Probability and Bayes’ Rule
  • 4.5. Sequential Bayesian Updating
  • 4.6. Independence of Events
• 5. Discrete Distributions
  • 5.1. Discrete Random Variables and Probability Mass Distributions
  • 5.2. Joint Probability Mass Functions
  • 5.3. Expected Value of a Discrete Random Variable
  • 5.4. Varianace of a Discrete Random Variable
  • 5.5. Covariance of Dependent Random Variables
  • 5.6. The Binomial Distribution
  • 5.7. The Poisson Distribution
• 6. Continuous Distributions
  • 6.1. Continuous Random Variables and Probability Density Functions
  • 6.2. Expected Value and Variance of Continuous Random Variables
  • 6.3. Cumulative Distribution Functions
  • 6.4. Normal Distribution
  • 6.5. Uniform Distribution
  • 6.6. Exponential Distribution
• 7. Sampling Distributions
  • 7.1. Statistics and Sampling Distributions
  • 7.2. Sampling Distribution for the Sample Mean
  • 7.3. The Central Limit Theorem (CLT)
  • 7.4. Understanding Binomial and Poisson Distributions through CLT
• 8. Experimental Design
  • 8.1. Experimental and Sampling Designs
  • 8.2. Experimental Design Principles
  • 8.3. Basic Types of Experimental Design
  • 8.4. Addressing Potential Flaws in Experimental Design
  • 8.5. Examples of Experimental Design
  • 8.6. Sampling Design
  • 8.7. Sampling Bias
• 9. Confidence Intervals and Bounds
  • 9.1. Introduction to Statistical Inference
  • 9.2. Confidence Intervals for the Population Mean, When σ is Known
  • 9.3. Precision of a Confidence Interval
  • 9.4. Confidence Bounds for the Poulation Mean When σ is Known
  • 9.5. Confidence Intervals and Bounds When σ is Unknown
• 10. Hypothesis Testing
  • 10.1. The Foundation of Hypothesis Testing
  • 10.2. Hypothesis Test for the Population Mean When σ is Known
  • 10.3. Connecting CI and HT; t-Test for μ When σ Is Unknown
  • 10.4. The Four Steps to Hypothesis Testing and Understanding the Result
• 11. Two Sample Procedures
  • 11.1. Statistical Inference for Two Samples
  • 11.2. Independent Two-Sample Analysis When Population Variances Are Known
  • 11.3. Independent Two-Sample Analysis - Pooled Variance Estimator
  • 11.4. Independent Two-Sample Analysis - No Equal Variance Assumption
  • 11.5. Paired Two-Sample Analysis
• 12. ANOVA
  • 12.1. Introduction to One-Way ANOVA
  • 12.2. Different Sources of Variability in ANOVA
  • 12.3. ANOVA F-Test and Its Relationship to Two-Sample t-Tests
  • 12.4. Multiple Comparison Procedures
• 13. Simple Linear Regression
  • 13.1. Introduction to Linear Regression
  • 13.2. Simple Linear Regression
  • 13.3. Model Diagnostics and Statistical Inference
  • 13.4. Prediction and Robustness

Course Resources

• Course Examinations
• Course Worksheets
• R / RStudio Guide and Function Reference