Simple Regression Analysis in Public Health
Master fundamental regression techniques for analyzing public health data. Learn linear, logistic, and Cox regression methods.
Master fundamental regression techniques for analyzing public health data. Learn linear, logistic, and Cox regression methods.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Biostatistics in Public Health Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Apply simple regression methods to analyze health data
Interpret relationships between outcomes and predictors
Perform statistical adjustments for confounding factors
Evaluate effect modification in research studies
Analyze survival data using Cox regression
Skills you'll gain
This course includes:
8 Hours PreRecorded video
9 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This comprehensive course teaches fundamental regression analysis techniques for public health research. Students learn various regression methods including linear, logistic, and Cox proportional hazards regression. The curriculum covers key concepts such as confounding, adjustment, and effect modification. Through practical examples from published studies, learners develop skills in interpreting data and performing statistical calculations for public health research.
Simple Regression Methods
Module 1 · 3 Hours to complete
Simple Logistic Regression
Module 2 · 2 Hours to complete
Simple Cox Proportional Hazards Regression
Module 3 · 2 Hours to complete
Confounding, Adjustment, and Effect Modification
Module 4 · 3 Hours to complete
Course Project
Module 5 · 2 Hours to complete
Fee Structure
Instructor
Associate Scientist and Biostatistics Educator at Johns Hopkins University
Dr. John McGready is an Associate Scientist in Biostatistics at Johns Hopkins University, where he has made significant contributions to both research and education since joining the faculty. He serves as the primary instructor for "Statistical Reasoning in Public Health I and II," teaching both on-campus and online courses, as well as "Statistical Methods in Public Health III." Dr. McGready is also the co-creator of intensive data analysis workshops at the School's Summer Institute of Epidemiology and Biostatistics. His research interests encompass statistical education, small sample properties of estimation methods, and advanced semi-parametric techniques applied to anthropomorphic data, among others.Recognized for his excellence in teaching, Dr. McGready has received multiple Golden Apple Awards and other accolades for his contributions to education in public health. Before his tenure at Johns Hopkins, he worked as a quantitative policy analyst focusing on criminal justice policy and taught high school mathematics. His diverse background enriches his approach to biostatistics and public health education, making him a valuable asset to the academic community.
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