University of Michigan - Linear Regression Modeling for Health Data
- Offered byCoursera
Linear Regression Modeling for Health Data at Coursera Overview
Duration | 13 hours |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Linear Regression Modeling for Health Data at Coursera Highlights
- Earn a certificate of completion
- Add to your LinkedIn profile
- 9 quizzes
Linear Regression Modeling for Health Data at Coursera Course details
- 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
- 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.
Linear Regression Modeling for Health Data at Coursera Curriculum
Principles of Statistical Modeling
Data Science for Health Research: Specialization Introduction
What is a Statistical Model? (Part 1)
What is a Statistical Model ? (Part 2)
Sampling: Accuracy Versus Precision
Confidence Intervals
Hypothesis Testing
Recap
What is a t-test Trying to Mimic?
Guided Practice: t-test part 1
Guided Practice: t-test Part 2
The t-test is a Regression Model
Meet Your Instructors
Welcome & Course Syllabus
Pre-Course Survey
Introduction To and How To Use Independent Guides
1.2 Discussion Prompt Suggested Answer
1.4 Independent Guide
End of Module 1 Discussion Prompt Suggested Answer
1.2 Practice Quiz
1.4 Practice Quiz
Module 1 Quiz
Meet Your Fellow Global Classmates
1.2 Discussion Prompt
End of Module 1 Discussion Prompt
Simple Linear Regression
Going Beyond the t-test
Confounding
Correlation
The Connection Between Correlation and Simple Linear Regression
Simple Linear Regression: The Main Idea
Guided Practice: Linear Regression
SLR: Estimation and Residuals
SLR: Prediction and Interpretation
Guided Practice: The lm() Function
Guided Practice: The summary() Function
Guided Practice: Pointing Back to the t-test
Simple Linear Regression: an Example
SLR with Binary Predictors is a t-test
Introduction to the BPUrban Data
2.1 Independent Guide
2.2a Independent Guide
New Reading
2.2b Independent Guide
2.2 Discussion Prompt Suggested Answer
Module 2 Discussion Prompt Suggested Answer
2.1 Practice Quiz
2.2 Practice Quiz
Comprehension Check
Module 2 Quiz
2.2 Discussion Prompt
End of Module 2 Discussion Prompt
Multiple Linear Regression
Introduction to Multiple Linear Regression or Regression with Multiple Predictors
Multiple Regression: The Basic Setup
Multiple Regression: Interpreting Coefficients
Guided Practice: How to Fit an MLR
Multiple Regression: Prediction Intervals Versus Confidence Intervals
Multiple Regression: Choosing From Among Variables
Using Multiple Regression to Answer Different Types of Questions
Evaluating Regression Models: MSE, Mallows Cp, and PRESS
3.2 Independent Guide
End of Module 3 Discussion Prompt Suggested Answer
Post-Course Survey
3.3 Practice Quiz
Module 3 Quiz
End of Module 3 Discussion Prompt
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