Regression Models for Analyzing Public Health Data
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Description
This exam tests knowledge of regression models essential for analyzing public health data, focusing on their selection, interpretation, and application in health contexts.
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Exam Details
Duration: 30 minutes
Prerequisites: Statistical Methods for Health Research, Advanced Biostatistics
Key Topics
- Linear Regression
- Logistic Regression
- Survival Analysis
- Model Interpretation
Learning Outcomes
- Explain Regression Models
- Interpret Model Outputs
- Relate Models to Health Outcomes
- Discuss Assumptions of Models
Full Description
This exam evaluates the application of regression models to analyze public health data, including linear regression, logistic regression, and survival analysis. Candidates must demonstrate proficiency in selecting appropriate models and interpreting results.
Regression models are vital tools for identifying relationships between variables, predicting health outcomes, and evaluating the effectiveness of interventions. Proficiency in these models is crucial for developing evidence-based public health policies.
The exam will assess the candidateโs ability to verbally explain the choice of regression models, articulate model assumptions, and interpret output in a public health context.
Candidates may also be required to discuss case studies that illustrate the application of regression analyses in real-world public health scenarios.
Sample Questions
- What are the key differences between linear and logistic regression?
- How would you interpret the coefficients obtained from a logistic regression analysis?
Field: Medical and Health Sciences
Subfield: Biostatistics
Specialization: Public Health Biostatistics
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