Multiple Regression Analysis in Public Health

Multiple Regression Analysis in Public Health

This course is part of Biostatistics in Public Health Specialization

Instructor: John McGready, PhD, MS

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17,656 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

322 reviews

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

322 reviews

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Practice multiple regression methods to determine relationships between an outcome and multiple predictors
Use the Spline approach for non-linear relationships with continuous predictors
Perform calculations with multiple predictor variables

Skills you'll gain

Details to know

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Add to your LinkedIn profile

Assessments

7 assignments

Taught in English

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This course is part of the Biostatistics in Public Health Specialization

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There are 4 modules in this course

Biostatistics is the application of statistical reasoning to the life sciences, and it's the key to unlocking the data gathered by researchers and the evidence presented in the scientific public health literature. In this course, you'll extend simple regression to the prediction of a single outcome of interest on the basis of multiple variables. Along the way, you'll be introduced to a variety of methods, and you'll practice interpreting data and performing calculations on real data from published studies. Topics include multiple logistic regression, the Spline approach, confidence intervals, p-values, multiple Cox regression, adjustment, and effect modification.

Within this module, an overview of multiple regression will be provided. Additionally, examples and applications will be examined. A practice quiz is provided to test your knowledge before completing the graded quiz.

What's included

9 videos2 readings2 assignments

9 videos Total 177 minutes

Multiple Regression: An Overview 13 minutes
Multiple Linear Regression: Some Examples 24 minutes
Multiple Linear Regression: Basics of Model Estimation, and Handling Uncertainty in the Resulting Estimates 15 minutes
Estimate Group Means and Mean Differences for Groups Who Differ in More than One Predictor with Multiple Linear Regression 10 minutes
The “Linearity” Assumption and Estimating Amount of Variability Explained by Multiple Predictors 22 minutes
Examples from the Literature 21 minutes
Investigating Effect Modification with Multiple Linear Regression (Forthcoming) 21 minutes
Investigating Effect Modification with Multiple Linear Regression—More Examples, For Those Interested 18 minutes
Additional Examples of Multiple Linear Regression 34 minutes

2 readings Total 25 minutes

Solutions to Practice Quiz on Multiple Linear Regression 15 minutes
Solutions to Summative Quiz 1 10 minutes

2 assignments Total 60 minutes

Practice Quiz: An Overview of Multiple Regression for Estimation, Adjustment, and Basic Prediction, and Multiple Linear Regression 30 minutes
An Overview of Multiple Regression for Estimation, Adjustment, and Basic Prediction, and Multiple Linear Regression 30 minutes

Module two covers examples of multiple logistic regression, basics of model estimates, and a discussion of effect modification. In addition to lectures, you will also be completing a practice quiz and graded quiz.

What's included

8 videos2 readings2 assignments

8 videos Total 173 minutes

Multiple Logistic Regression: Some Examples 25 minutes
Multiple Logistic Regression: Basics of Model Estimation, and Handling Uncertainty in the Resulting Estimates 19 minutes
Estimating Group Odds and Proportions, and Odds Ratios for Groups Who Differ in More than One Predictor with Multiple Linear Regression 25 minutes
The “Linearity” Assumption and A Brief Note about Prediction with Multiple Logistic Regression 28 minutes
Examples from the Literature 27 minutes
Investigating Effect Modification with Multiple Logistic Regression, Part 1 19 minutes
Investigating Effect Modification with Multiple Logistic Regression, Part 2 10 minutes
Additional Examples 21 minutes

2 readings Total 30 minutes

Solutions to Practice Quiz Items 1-9 20 minutes
Solutions to Quiz on Logistic Regression 10 minutes

2 assignments Total 60 minutes

Practice Quiz: Multiple Logistic Regression 30 minutes
Multiple Logistic Regression 30 minutes

The last module for this class focuses on multiple Cox regression, the “Linearity” assumption, examples, and applications. You will complete a practice quiz, graded quiz, and project.

What's included

5 videos2 readings2 assignments

5 videos Total 95 minutes

Some Examples 17 minutes
Basics of Model Estimation and Handling Uncertainty in the Resulting Estimates 18 minutes
Estimating Hazard Ratios for Groups who Differ in more than one Predictor with Multiple Cox Regression 12 minutes
The "Linearity" Assumption and Prediction with Multiple Cox Regression 21 minutes
Examples from the Literature 26 minutes

2 readings Total 30 minutes

Solutions To Multiple Cox Regression Practice Quiz 20 minutes
Solutions: Summative Quiz (Multiple Cox Regression) 10 minutes

2 assignments Total 60 minutes

Practice Quiz: Multiple Cox Regression 30 minutes
Multiple Cox Regression, and Course Concepts 30 minutes

During this module, you get the chance to demonstrate what you've learned by putting yourself in the shoes of biostatistical consultant on two different studies, one about self-administration of injectable contraception and one about medical appointment scheduling in Brazil. The two research teams have asked you to help them interpret previously published results in order to inform the planning of their own studies. If you've already taken other courses in this specialization, then this scenario will be familiar.

What's included

1 reading1 assignment

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Instructor

Instructor ratings

(83 ratings)

John McGready, PhD, MS

Johns Hopkins University

6 Courses 101,861 learners

Offered by

Johns Hopkins University

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Reviewed on Jul 1, 2020

one of the finest courses on biostatistics, in order to get refreshed with the core concepts and applications!

Reviewed on Apr 21, 2023

Recommended for beginners in the field. Instructor John McGready presents the otherwise complex concepts of Biostatistics straightforwardly, with many examples and a perfect pace.

Reviewed on Aug 1, 2019

Incredible way of teaching biostatistics using by just adding more complexity to the same exercises in a gradual form!

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