Simple Regression Analysis in Public Health
- 4.7
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Course Summary
Learn how to use simple regression analysis in public health research with this course. Develop your skills in statistical modeling and analysis.Key Learning Points
- Understand the basics of regression analysis
- Learn how to use regression analysis in public health research
- Develop skills in statistical modeling and analysis
Job Positions & Salaries of people who have taken this course might have
- USA: $63,000
- India: ₹4,90,000
- Spain: €28,000
- USA: $63,000
- India: ₹4,90,000
- Spain: €28,000
- USA: $54,000
- India: ₹3,80,000
- Spain: €26,000
- USA: $63,000
- India: ₹4,90,000
- Spain: €28,000
- USA: $54,000
- India: ₹3,80,000
- Spain: €26,000
- USA: $105,000
- India: ₹12,00,000
- Spain: €50,000
Related Topics for further study
Learning Outcomes
- Understand the basics of regression analysis and its application in public health research
- Develop skills in statistical modeling and analysis
- Apply regression analysis techniques to real-world public health problems
Prerequisites or good to have knowledge before taking this course
- Basic understanding of statistics
- Familiarity with statistical software like R or Stata
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Multivariate Data Analysis in Public Health Research
- Applied Regression Analysis in Public Health Research
Related Education Paths
Notable People in This Field
- Johns Hopkins Bloomberg School of Public Health
- World Health Organization
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, we'll focus on the use of simple regression methods to determine the relationship between an outcome of interest and a single predictor via a linear equation. 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 logistic regression, confidence intervals, p-values, Cox regression, confounding, adjustment, and effect modification.
Knowledge
- Practice simple regression methods to determine relationships between an outcome and a predictor
- Recognize confounding in statistical analysis
- Perform estimate adjustments
Outline
- Simple Regression Methods
- Introduction
- Simple Regression: An Overview
- Simple Linear Regression with a Binary (or Nominal Categorical) Predictor
- Simple Linear Regression with a Categorical Predictor
- Simple Linear Regression with a Continuous Predictor
- Simple Linear Regression Model: Estimating the Regression Equation—Accounting for Uncertainty in the Estimates
- Measuring the Strength of a Linear Association
- Additional Examples
- Solutions to Linear Regression Practice Quiz
- Quiz 1 Solution
- Simple Linear Regression
- Simple Logistic Regression
- Introduction
- Simple Logistic Regression: An Overview
- Simple Logistic Regression with a Binary (or Categorical) Predictor
- Simple Logistic Regression with a Continuous Predictor
- Simple Logistic Regression: Accounting for Uncertainty in the Estimates
- Estimating Risk and Functions of Risk from Logistic Regression Results
- Additional Examples
- Solutions to Practice Quiz on Simple Logistic Regression
- Quiz 2 Solutions
- Logistic Regression
- Simple Cox Proportional Hazards Regression
- Introduction
- Simple Cox Regression: An Overview
- Simple Cox Regression with a Binary (or Categorical) Predictor
- Simple Cox Regression with a Continuous Predictor
- Simple Cox Regression: Accounting for Uncertainty in the Estimates
- Estimating Survival Curves from Cox Regression Results
- Additional Examples
- Solutions for Practice Quiz on Simple Cox Regression
- Solutions to Summative Quiz 3
- Simple Cox Regression
- Confounding, Adjustment, and Effect Modification
- Introduction
- Confounding: A Formal Definition and Some Examples
- Adjusted Estimates: Presentation, Interpretation, and Utility for Assessing Confounding
- Adjusted Estimates: The General Idea Behind the Computations
- Additional Examples
- Effect modification: Introduction with an analytic example
- Effect modification: Tree damage and elevation example
- Effect modification: Examples from the literature
- Confounding versus effect modification: A review
- Additional examples
- Solutions to Summative Quiz 4
- Practice Quiz: Confounding and Effect Modification
- Confounding and Effect Modification
- Course Project
- Biostatistical Consulting Project
- Course Project Quiz
Summary of User Reviews
This course on simple regression analysis in public health received high praise from many users. The course covers basic statistical concepts and their application in public health research. One key aspect that many users appreciated was the clear explanations and practical examples provided throughout the course.Pros from User Reviews
- Clear explanations and practical examples
- Good introduction to basic statistical concepts
- Applicable to public health research
- Engaging and interactive course content
- Great for beginners in statistics and public health
Cons from User Reviews
- Some users found the pace too slow
- Not enough in-depth coverage of advanced topics
- Course could benefit from more real-world case studies
- Limited interaction with instructors
- Lack of hands-on practice with statistical software
John McGready, PhD, MS
4.7 Raiting
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