Linear Regression in R for Public Health
Master statistical modeling using R to analyze health data. Learn correlation, regression, and model building for public health research.
Master statistical modeling using R to analyze health data. Learn correlation, regression, and model building for public health research.
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 Statistical Analysis with R for 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.
4.8
(499 ratings)
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English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Describe when linear regression models are appropriate
Read and check datasets using R software
Fit multiple linear regression models with interactions
Interpret model outputs and check assumptions
Develop robust model building strategies
Skills you'll gain
This course includes:
1.2 Hours PreRecorded video
11 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course focuses on linear regression analysis using R for public health applications. Students learn to assess correlations, build statistical models, and interpret results to understand disease factors. The curriculum covers single and multiple regression, interaction terms, and model building strategies. Through hands-on practice with real health data, learners develop skills in data preparation, assumption testing, and results interpretation.
INTRODUCTION TO LINEAR REGRESSION
Module 1 · 4 Hours to complete
Linear Regression in R
Module 2 · 3 Hours to complete
Multiple Regression and Interaction
Module 3 · 3 Hours to complete
MODEL BUILDING
Module 4 · 2 Hours to complete
Fee Structure
Instructors
Expert in Medical Statistics and Healthcare Quality
Prof. Alex Bottle is a Professor in Medical Statistics and co-director of the Dr Foster Unit at Imperial College London. His research is centered on measuring and understanding variations in healthcare quality and safety through the use of large databases. In addition to his research, Prof. Bottle teaches across various undergraduate and postgraduate programs, supervises and examines PhD students in fields such as surgery, cardiovascular risk, and digital health, and provides statistical training to the UK public sector.
Senior Lecturer in Medical Statistics and Clinical Trials
Dr. Victoria Cornelius is a Reader in Medical Statistics and Clinical Trials at Imperial College London, where she also serves as the Deputy Head of Statistics in the Imperial Clinical Trials Unit (ICTU). She leads the Child Health portfolio and the Statistical Methods Research Group within ICTU. With extensive experience in evaluating drug interventions across various therapeutic areas, including asthma, allergy, mental health, and cancer, Dr. Cornelius is dedicated to advancing the field of medical statistics. Her research focuses on innovative statistical methods, particularly in time-to-event signal detection for identifying adverse drug reactions and effectively presenting harm information in clinical trials.Passionate about translating best statistical practices into applied research, Dr. Cornelius aims to enhance the quality of research outcomes. She is actively involved in teaching and mentoring students in statistical methodologies, ensuring that they are well-equipped to contribute to clinical research and public health initiatives. Additionally, she offers a course on "Linear Regression in R for Public Health," which reflects her commitment to educating future researchers on the importance of robust statistical analysis in healthcare settings. Through her work, Dr. Cornelius strives to improve research methodologies that ultimately lead to better patient care and safety.
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4.8 course rating
499 ratings
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