Course Summary
Learn how to use linear regression to analyze public health data in R with this course. Gain the skills to conduct statistical analysis, interpret results, and create visualizations.Key Learning Points
- Learn how to use linear regression to analyze public health data
- Gain skills in statistical analysis and visualization
- Apply your knowledge to real-world scenarios
Job Positions & Salaries of people who have taken this course might have
- Public Health Analyst
- USA: $65,000 - $110,000
- India: ₹500,000 - ₹1,500,000
- Spain: €25,000 - €45,000
- Research Scientist
- USA: $75,000 - $130,000
- India: ₹600,000 - ₹2,000,000
- Spain: €28,000 - €50,000
- Data Analyst
- USA: $55,000 - $95,000
- India: ₹400,000 - ₹1,200,000
- Spain: €22,000 - €40,000
Related Topics for further study
Learning Outcomes
- Ability to conduct linear regression analysis on public health data
- Interpretation and visualization of results
- Application of learned skills to real-world scenarios
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics and programming
- Access to R programming software
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
- Hands-on exercises
Similar Courses
- Applied Data Science with Python
- Introduction to Data Science in Python
- Data Analysis and Interpretation
Related Education Paths
- Master of Public Health (MPH)
- Master of Science in Biostatistics
- Graduate Certificate in Public Health
Notable People in This Field
- Statistician and Founder of FiveThirtyEight
- Public Health Expert and Founder of Gapminder
- Pioneer of Modern Nursing and Public Health
Related Books
Description
Welcome to Linear Regression in R for Public Health!
Knowledge
- Describe when a linear regression model is appropriate to use
- Read in and check a data set's variables using the software R prior to undertaking a model analysis
- Fit a multiple linear regression model with interactions, check model assumptions and interpret the output
Outline
- INTRODUCTION TO LINEAR REGRESSION
- Welcome to the Course
- Pearson’s Correlation Part I
- Pearson’s Correlation Part II
- Intro to Linear Regression: Part I
- Intro to Linear Regression: Part II
- Linear Regression and Model Assumptions: Part I
- Linear Regression and Model Assumptions: Part II
- About Imperial College London & the Team
- How to be successful in this course
- Grading policy
- Data set and Glossary
- Additional Reading
- Linear Regression Models: Behind the Headlines
- Linear Regression Models: Behind the Headlines: Written Summary
- Warnings and precautions for Pearson's correlation
- Introduction to Spearman correlation
- Linear Regression Models: Behind the Headlines
- Correlations
- Spearman Correlation
- Practice Quiz on Linear Regression
- End of Week Quiz
- Linear Regression in R
- Introduction to Week 2
- Fitting the linear regression
- Multiple Regression
- Recap on installing R
- Assessing distributions and calculating the correlation coefficient in R
- Feedback
- How to fit a regression model in R
- Feedback
- Fitting the Multiple Regression in R
- Feedback
- Summarising correlation and linear regression
- Linear Regression
- End of Week Quiz
- Multiple Regression and Interaction
- Introduction to Key Dataset Features: Part I
- Introduction to Key Dataset Features: Part II
- Interactions between binary variables
- Interactions between binary and continuous variables
- How to assess key features of a dataset in R
- How to check your data in R
- Good Practice Steps
- Practice with R: Run a Good Practice Analysis
- Practice with R: Run Multiple Regression
- Feedback
- Practice with R: Running and interpreting a multiple regression
- Feedback
- Additional Reading
- Fitting and interpreting model results
- Interpretation of interactions
- MODEL BUILDING
- Intro to Model Development
- Variable Selection
- Developing a Model Building Strategy
- Summary of developing a Model Building Strategy
- Summary of Course
- Feedback
- Further details of limitations of stepwise
- How many predictors can I include?
- Practice with R: Fitting the final model
- Feedback on developing the model
- Final R Code
- Problems with automated approaches
- End of Course Quiz
Summary of User Reviews
A popular course on Coursera for learning linear regression in public health with great reviews, focused on practical applications and real-world examples.Key Aspect Users Liked About This Course
The course is highly practical, and uses real-world examples to teach linear regression in the context of public health.Pros from User Reviews
- The course instructors are knowledgeable and engaging
- The course content is well-structured and easy to follow
- The course provides plenty of hands-on experience, which is great for learning linear regression
Cons from User Reviews
- Some users found the course to be too basic, and not challenging enough
- The course does not cover advanced topics in linear regression
- The course may not be suitable for those who do not have a background in statistics or data analysis
Alex Bottle
4.8 Raiting
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