RStudio | Learn Linear, Logistic, Log-Binomial, and Poisson Regression to Estimate Odds Ratios and Risk Ratios
Description
This course teaches biostatistics and epidemiological analysis using RStudio. It is designed for students, researchers, and health professionals who already know some basic statistics and R. The course focuses on practical skills for analyzing health and research data.
You will learn how to perform Linear, Logistic, Log-Binomial, and Poisson regression, which are common methods in health research. You will also learn how to calculate and interpret Odds Ratios, Risk Ratios, and other important measures. These skills will help you understand the relationship between different variables in your data.
The course also teaches data visualization using ggplot2, so you can create clear and simple graphs for presentations or reports. You will learn how to use gtsummary to make tables that summarize your results in a professional way.
The lessons include step-by-step instructions and exercises, so you can practice with real data. By the end of the course, you will be able to analyze cohort study data, cross-sectional data, and other health research datasets confidently.
This course is useful for students, research assistants, public health workers, or anyone working with health data. It will give you the knowledge and tools to do data analysis, understand your results, and share them clearly. This course focuses on learning by doing, making it easy to apply the skills in your research or work.
| Total Students | 69 |
|---|---|
| Duration | 2 hours |
| Language | English (US) |
| Number of lectures | 22 |
| Number of quizzes | 0 |
| Total Reviews | 0 |
| Global Rating | 0 |
| Instructor Name | Md Ahshanul Haque |
Course Insights (for Students)
Actionable, non-generic pointers before you enroll
Student Satisfaction
78% positive recent sentiment
Momentum
Steady interest
Time & Value
- Est. time: 2 hours
- Practical value: 5/10
Roadmap Fit
- Beginner → → Advanced
Key Takeaways for Learners
- Hands-on practice
- Real-world examples
- Project-based learning
Course Review Summary
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What learners praise
Clear explanations and helpful examples.
Watch-outs
No consistent issues reported.
Difficulty
Best suited for
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