Multiple Regression Analysis in Public Health
- 4.7
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Course Summary
Learn how to use multiple regression analysis in public health research to identify patterns and relationships between variables and make informed decisions.Key Learning Points
- Understand the principles and techniques of multiple regression analysis
- Learn how to use regression analysis to identify risk factors and evaluate interventions
- Develop skills in data analysis and interpretation for public health research
Related Topics for further study
Learning Outcomes
- Understand the principles and techniques of multiple regression analysis
- Develop skills in data analysis and interpretation for public health research
- Apply regression analysis to identify risk factors and evaluate interventions
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with public health research
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Applied Regression Analysis
- Data Analysis in Public Health
Related Education Paths
Related Books
Description
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.
Knowledge
- 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
Outline
- An Overview of Multiple Regression for Estimation, Adjustment, and Basic Prediction, and Multiple Linear Regression
- Multiple Regression: An Overview
- Multiple Linear Regression: Some Examples
- Multiple Linear Regression: Basics of Model Estimation, and Handling Uncertainty in the Resulting Estimates
- Estimate Group Means and Mean Differences for Groups Who Differ in More than One Predictor with Multiple Linear Regression
- The “Linearity” Assumption and Estimating Amount of Variability Explained by Multiple Predictors
- Examples from the Literature
- Investigating Effect Modification with Multiple Linear Regression (Forthcoming)
- Investigating Effect Modification with Multiple Linear Regression—More Examples, For Those Interested
- Additional Examples of Multiple Linear Regression
- Solutions to Practice Quiz on Multiple Linear Regression
- Solutions to Summative Quiz 1
- Practice Quiz: An Overview of Multiple Regression for Estimation, Adjustment, and Basic Prediction, and Multiple Linear Regression
- An Overview of Multiple Regression for Estimation, Adjustment, and Basic Prediction, and Multiple Linear Regression
- Multiple Logistic Regression
- Multiple Logistic Regression: Some Examples
- Multiple Logistic Regression: Basics of Model Estimation, and Handling Uncertainty in the Resulting Estimates
- Estimating Group Odds and Proportions, and Odds Ratios for Groups Who Differ in More than One Predictor with Multiple Linear Regression
- The “Linearity” Assumption and A Brief Note about Prediction with Multiple Logistic Regression
- Examples from the Literature
- Investigating Effect Modification with Multiple Logistic Regression, Part 1
- Investigating Effect Modification with Multiple Logistic Regression, Part 2
- Additional Examples
- Solutions to Practice Quiz Items 1-9
- Solutions to Quiz on Logistic Regression
- Practice Quiz: Multiple Logistic Regression
- Multiple Logistic Regression
- Multiple Cox Regression
- Some Examples
- Basics of Model Estimation and Handling Uncertainty in the Resulting Estimates
- Estimating Hazard Ratios for Groups who Differ in more than one Predictor with Multiple Cox Regression
- The "Linearity" Assumption and Prediction with Multiple Cox Regression
- Examples from the Literature
- Soluitons To Multiple Cox Regression Practice Quiz
- Solutions: Summative Quiz (Multiple Cox Regression)
- Practice Quiz: Multiple Cox Regression
- Multiple Cox Regression, and Course Concepts
- Course Project
- Biostatistical Consulting Project
- Project
Summary of User Reviews
Read reviews of Multiple Regression Analysis in Public Health from the world's top universities and institutions. Overall rating of the course is great. Many users appreciated the in-depth coverage of the topic.Pros from User Reviews
- In-depth coverage of the topic
- Well-structured content
- Good balance between theory and application
- Great resources and materials
- Engaging and knowledgeable instructors
Cons from User Reviews
- Some users found the course too technical
- Few users had trouble with the quizzes
- Some users felt the course lacked practical examples
- Few users didn't like the pace of the course
- Some users thought that the course was too lengthy
John McGready, PhD, MS
4.7 Raiting
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