Video Learning Platform

Open the STAT 350 Video Learning Platform

What it provides

  • All lecture videos 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.

  • Search/filter to locate topics across videos.

Notes

  • Best viewed on a desktop browser.

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

Course Introduction & Overview

Welcome to STAT 350: Introduction to Statistics.

Statistics isn’t just about crunching numbers and creating graphs. At its core, statistics is about understanding uncertainty and making well-informed decisions based on evidence. Whether you’re pursuing engineering, life sciences, social sciences, or just sharpening your critical thinking skills, this course is designed to equip you with the fundamental tools you need.

Why Statistics? Why Now?

We live in an era dominated by data. Practically 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 decisions. Incorrect interpretation can have serious real-world consequences.

  • Understanding uncertainty: Life is unpredictable, and data often reflect that. Statistical methods are essential to quantify, understand, and communicate uncertainty clearly and accurately.

  • The backbone of AI and machine learning: All modern AI models, including tools like ChatGPT, rely heavily on probability theory and statistical modeling. These techniques are critical for building and improving AI systems and interpreting their results accurately.

Course Roadmap

Here’s a comprehensive breakdown of what you’ll learn in each chapter:

Chapters

What you’ll explore

Connections with other chapters

Ch 1–3

Descriptive Statistics

Visualize and summarize data. Find patterns effectively.

Systematically describe the visible surface of data

Ch 4–6

Probability

Theoretical foundation on the system which produces randomness and uncertainty in everyday phenomena.

Understand the data-generating mechanism which results in data shown in Ch 1-3

Ch 7

Sampling Distributions

How and why do sample results vary? What is their relation to the rules governing data generation?

Bridges Ch 1-3 and Ch 4-6

Ch 8

Experimental Design

Design solid studies and avoiding common pitfalls.

Ensures necessary conditions are met for Ch 7 to hold.

Ch 9

Confidence Intervals and Bounds

Identify regions that likely contain the true parameter.

Foundational forms of statistical inference. Both build on Ch 7.

Ch 10

Hypothesis Testing

Evaluate claims, understand evidence, and make decisions in the face of uncertainty.

Ch 11

Two-Sample Methods

Compare the centers of two groups.

Advanced inference methods based on Ch 9-10

Ch 12

One-way Analysis of Variance (ANOVA)

Compare the centers of multiple groups simultaneously.

Ch 13

Simple Linear Regression

Model the association of two variables with a line.

A Clear Note on Using AI

AI tools like ChatGPT and GitHub Copilot are powerful resources. However, relying on AI purely to save time without developing deep understanding will leave you vulnerable in the job market. The future workforce demands critical thinkers capable of leveraging AI, not those easily replaced by it.

When using AI to assist you in this course,

  • Clearly learn the advantages and limitations of current AI resources in generating academic contents.

  • Verifying AI-generated content against externel resources before use is strongly encouraged.

  • You must stay within project specifications. In this course that means you must not go outside the material of the computer assignment tutorials.

  • When including AI-generated content in an assignment, whether directly or indirectly, you must include a note clearly citing where and how you used it:

  • For general guidance, see APA guidelines for citing AI-generated content.

A Guide for Proper AI Citation

  • State the AI tool used (e.g., ChatGPT, GitHub Copilot).

  • Describe your prompt.

  • Clarify how you verified and/or modified the AI-generated content.

Getting Started: A Checklist

Here’s a checklist of what you need to do next:

✔❓

To do

Description

Familiarize yourself with course organization

Review the syllabus carefully. Mark the exam dates and the weekly deadlines.

Explore the course schedule

Traditional Lecture, Flipped Section, Asynchronous Online Section, Winter Session

Set up RStudio

We use R in RStudio. Set up your environment:

Bookmark the three course sites

Edfinity for homework; Brightspace for communication; Course website for materials.

Start the first assignments

Syllabus Homework, Homework 1, and Computer Assignment 1 are due near the start of the semester. Mark deadlines and begin early.

Homework Assignments on Edfinity

The homework assignments will be distributed via the Edfinity platform. Edfinity provides interactive problems with partially automated grading and instant feedback for the auto-graded questions. You must sign up through any link to Edfinity assignments on Brightspace. There will be two types of Edfinity assignments:

  • Standard homework assignments: Short-answer and multiple-choice questions, no external datasets required.

  • Computer assignments: These involve data analysis in R, submitted directly on Edfinity.

Miscellaneous Tips

Please note ‼️

  • The slides provided are dynamic and should always be downloaded and viewed in slideshow mode to display all content correctly.

  • For comprehensive review (especially useful for the final exam), refer to the interactive Comprehensive Statistics Guide.

Looking forward to a productive semester. Let’s dive into statistics!