This course is part of Statistical Analysis in R.
This advanced course, part of the Statistical Analysis in R professional certificate, extends beyond basic linear regression to cover complex statistical modeling scenarios. Students learn to analyze binary, count, and categorical response variables using Generalized Linear Models (GLMs) and explore hierarchical experimental designs. The course balances theoretical understanding with practical R programming implementation, covering topics from exploratory data analysis to power analysis and experimental design. Emphasis is placed on understanding when and how to apply different statistical methods effectively.
Instructors:
English
English
What you'll learn
Implement advanced data visualization techniques using R
Apply Generalized Linear Models for different response variables
Develop mixed effects linear regression models
Conduct power analysis for experimental design
Perform multivariate analysis using various GLM types
Evaluate model assumptions and diagnostics
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Module Description
The course provides comprehensive coverage of advanced statistical modeling techniques using R. Topics include exploratory data analysis, generalized linear models for various response types (binary, count, categorical), mixed effects linear regression models, and power analysis. Students learn both theoretical foundations and practical implementation through R programming, with emphasis on model selection, diagnostics, and interpretation. The curriculum includes hands-on practice with real-world data analysis scenarios.
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Statistical Analysis in R
Instructor

4 Courses
A Distinguished Leader in Statistical Modeling and Epidemiology
Elena Moltchanova serves as Professor of Statistics and Head of the Statistical Consulting Unit at the University of Canterbury, where she has established herself as a leading expert in applied Bayesian statistics and spatial modeling. Her academic journey began at the University of Helsinki, Finland, where she completed her MSc in Statistics, followed by a PhD from the University of Jyvaskyla focusing on applications of spatial statistics in epidemiology. Her career includes significant contributions at IIASA's Ecosystems Services and Management Program, where she first joined as a Young Scientists Summer Program participant in 2001, earning the Michailevich Scholarship for her work on image restoration. Since joining the University of Canterbury in 2011, she has published extensively, with over 50 papers spanning epidemiology of chronic diseases, extreme event modeling, and forest ecology. Her research impact is evidenced by over 9,300 citations and an h-index of 40. Her expertise spans multiple statistical software platforms and programming languages, which she applies to diverse fields including climate modeling, rare extreme events analysis, and epidemiological studies. As Head of the Statistical Consulting Unit, she continues to bridge theoretical statistics with practical applications while maintaining active research collaborations across international institutions.
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Frequently asked questions
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