Linear Regression Modeling for Health Data

Linear Regression Modeling for Health Data

This course is part of Data Science for Health Research Specialization

Instructors: Philip S. Boonstra

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

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

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

9 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Data Science for Health Research Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 3 modules in this course

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.

This module gives you 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 you to the core ideas of traditional statistical inference and reasoning. At the end of the module, you will have an introductory understanding of important terms such as 'sample-to-population' (STOP) principle, sampling variation, and measures of statistical uncertainty. You will also get your first look at the ever popular t-test.

What's included

11 videos7 readings3 assignments3 discussion prompts

11 videos Total 119 minutes

Data Science for Health Research: Specialization Introduction 6 minutes
What is a Statistical Model? (Part 1) 18 minutes
What is a Statistical Model ? (Part 2) 14 minutes
Sampling: Accuracy Versus Precision 8 minutes
Confidence Intervals 11 minutes
Hypothesis Testing 15 minutes
Recap 6 minutes
What is a t-test Trying to Mimic? 10 minutes
Guided Practice: t-test part 1 11 minutes
Guided Practice: t-test Part 2 12 minutes
The t-test is a Regression Model 7 minutes

7 readings Total 63 minutes

Meet Your Instructors 3 minutes
Welcome & Course Syllabus 10 minutes
Pre-Course Survey 10 minutes
Introduction To and How To Use Independent Guides 10 minutes
1.2 Discussion Prompt Suggested Answer 10 minutes
1.4 Independent Guide 10 minutes
End of Module 1 Discussion Prompt Suggested Answer 10 minutes

3 assignments Total 90 minutes

Module 1 Quiz 30 minutes
1.2 Practice Quiz 30 minutes
1.4 Practice Quiz 30 minutes

3 discussion prompts Total 30 minutes

Meet Your Fellow Global Classmates 10 minutes
1.2 Discussion Prompt 10 minutes
End of Module 1 Discussion Prompt 10 minutes

This module takes you beyond t-test into linear regression. By the end of the module, you will understand how linear regression is a generalization of the t-test.

What's included

13 videos6 readings4 assignments2 discussion prompts

13 videos Total 123 minutes

Going Beyond the t-test 9 minutes
Confounding 7 minutes
Correlation 12 minutes
The Connection Between Correlation and Simple Linear Regression 7 minutes
Simple Linear Regression: The Main Idea 9 minutes
Guided Practice: Linear Regression 10 minutes
SLR: Estimation and Residuals 8 minutes
SLR: Prediction and Interpretation 10 minutes
Guided Practice: The lm() Function 12 minutes
Guided Practice: The summary() Function 11 minutes
Guided Practice: Pointing Back to the t-test 14 minutes
Simple Linear Regression: an Example 7 minutes
SLR with Binary Predictors is a t-test 8 minutes

6 readings Total 60 minutes

Introduction to the BPUrban Data 10 minutes
2.1 Independent Guide 10 minutes
2.2a Independent Guide 10 minutes
2.2b Independent Guide 10 minutes
2.2 Discussion Prompt Suggested Answer 10 minutes
Module 2 Discussion Prompt Suggested Answer 10 minutes

4 assignments Total 120 minutes

Comprehension Check 30 minutes
Module 2 Quiz 30 minutes
2.1 Practice Quiz 30 minutes
2.2 Practice Quiz 30 minutes

2 discussion prompts Total 20 minutes

2.2 Discussion Prompt 10 minutes
End of Module 2 Discussion Prompt 10 minutes

A key reason that linear regression is so powerful is that it allows to adjust for multiple predictors at the same time. In Module 3, you will learn how to fit regression models for multiple predictors. You will see how to interpret the resulting model and how to use it to answer different questions about your data.

What's included

8 videos3 readings2 assignments1 discussion prompt

8 videos Total 58 minutes

Introduction to Multiple Linear Regression or Regression with Multiple Predictors 7 minutes
Multiple Regression: The Basic Setup 3 minutes
Multiple Regression: Interpreting Coefficients 6 minutes
Guided Practice: How to Fit an MLR 16 minutes
Multiple Regression: Prediction Intervals Versus Confidence Intervals 5 minutes
Multiple Regression: Choosing From Among Variables 5 minutes
Using Multiple Regression to Answer Different Types of Questions 7 minutes
Evaluating Regression Models: MSE, Mallows Cp, and PRESS 9 minutes

3 readings Total 30 minutes

3.2 Independent Guide 10 minutes
End of Module 3 Discussion Prompt Suggested Answer 10 minutes
Post-Course Survey 10 minutes

2 assignments Total 60 minutes

Module 3 Quiz 30 minutes
3.3 Practice Quiz 30 minutes

1 discussion prompt Total 10 minutes

End of Module 3 Discussion Prompt 10 minutes

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Instructors

Philip S. Boonstra

University of Michigan

4 Courses 4,236 learners

Offered by

University of Michigan

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