Associate Professor, Biostatistics
Linear Regression Modeling for Health Data
What You'll Learn
- Become knowledgeable about the concept of statistical modeling and the basics of statistical inference
- Recognize, fit, and interpret a simple linear regression model
- Develop intuition to fit and interpret a multiple regression model
3 Modules
12 Hours
4 hrs per module (approx.)
Rating
About Linear Regression Modeling for Health Data
This course provides learners with a first look at the world of statistical modeling. It begins with a high-level overview of different philosophies on the question of 'what is a statistical model' and introduces learners to the core ideas of traditional statistical inference and reasoning. Learners will get their first look at the ever-popular t-test and delve further into linear regression. They will also learn how to fit and interpret regression models for a continuous outcome with multiple predictors. All concepts taught in this course will be covered with multiple modalities: slide-based lectures, guided coding practice with the instructor, and independent but structured exercises.
Skills You'll Gain
- Data Analysis
- Linear Regression
- Probability Distribution
- Statistical Analysis
- Statistical Programming
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to Linear Regression Modeling for Health Data, a course designed for researchers, scientists, public health professionals, and data-driven decision-makers. This course, part of the Data Science for Health Research series, introduces the foundations of statistical modeling, including traditional statistical inference, t-tests, and linear regression with multiple predictors. Learners will gain hands-on experience fitting and interpreting models through lectures, guided coding exercises, and structured independent practice. This course provides practical skills for analyzing health data effectively.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Principles of Statistical Modeling
- Video: Data Science for Health Research: Specialization Introduction
- Reading: Meet Your Instructors
- Reading: Welcome & Course Syllabus
- Discussion Prompt: Meet Your Fellow Global Classmates
- Reading: Pre-Course Survey
- Reading: Introduction To and How To Use Independent Guides
- Video: What is a Statistical Model? (Part 1)
- Video: What is a Statistical Model ? (Part 2)
- Graded Assignment: 1.2 Practice Quiz
- Discussion Prompt: 1.2 Discussion Prompt
- Reading: 1.2 Discussion Prompt Suggested Answer
- Video: Sampling: Accuracy Versus Precision
- Video: Confidence Intervals
- Video: Hypothesis Testing
- Video: Recap
- Video: What is a t-test Trying to Mimic?
- Video: Guided Practice: t-test part 1
- Video: Guided Practice: t-test Part 2
- Reading: 1.4 Independent Guide
- Video: The t-test is a Regression Model
- Graded Assignment: 1.4 Practice Quiz
- Discussion Prompt: End of Module 1 Discussion Prompt
- Reading: End of Module 1 Discussion Prompt Suggested Answer
Module 2: Simple Linear Regression
- Video: Going Beyond the t-test
- Video: Confounding
- Video: Correlation
- Video: The Connection Between Correlation and Simple Linear Regression
- Video: Simple Linear Regression: The Main Idea
- Reading: Introduction to the BPUrban Data
- Video: Guided Practice: Linear Regression
- Reading: 2.1 Independent Guide
- Graded Assignment: 2.1 Practice Quiz
- Video: SLR: Estimation and Residuals
- Video: SLR: Prediction and Interpretation
- Video: Guided Practice: The lm() Function
- Video: Guided Practice: The summary() Function
- Reading: 2.2a Independent Guide
- Video: Guided Practice: Pointing Back to the t-test
- Reading: 2.2b Independent Guide
- Video: Simple Linear Regression: an Example
- Video: SLR with Binary Predictors is a t-test
- Graded Assignment: 2.2 Practice Quiz
- Discussion Prompt: 2.2 Discussion Prompt
- Reading: 2.2 Discussion Prompt Suggested Answer
- Discussion Prompt: End of Module 2 Discussion Prompt
- Reading: Module 2 Discussion Prompt Suggested Answer
Module 3: Multiple Linear Regression
- Video: Introduction to Multiple Linear Regression or Regression with Multiple Predictors
- Video: Multiple Regression: The Basic Setup
- Video: Multiple Regression: Interpreting Coefficients
- Video: Guided Practice: How to Fit an MLR
- Reading: 3.2 Independent Guide
- Video: Multiple Regression: Prediction Intervals Versus Confidence Intervals
- Video: Multiple Regression: Choosing From Among Variables
- Video: Using Multiple Regression to Answer Different Types of Questions
- Video: Evaluating Regression Models: MSE, Mallows Cp, and PRESS
- Graded Assignment: 3.3 Practice Quiz
- Discussion Prompt: End of Module 3 Discussion Prompt
- Reading: End of Module 3 Discussion Prompt Suggested Answer
- Reading: Post-Course Survey
Grading Policy
Learners must achieve an overall grade of 80% to pass and earn the certificate. The course grade is based on four quizzes: three worth 30% each, and one comprehension check worth 10%.
Professor of Epidemiology
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Intermediate Level
There are no formal requirements to take this course. It is preferred that learners have a basic understanding of algebra and probability.
