This course is part of Statistical Analysis in R.
This comprehensive course provides a practical introduction to statistical inference and modeling using R programming. Students learn theoretical foundations and hands-on implementation of key statistical concepts, including sampling distributions, hypothesis testing, ANOVA, and multivariate analysis. The course emphasizes understanding both why methods work and when to apply them, combining theoretical knowledge with practical R programming skills. Designed for those with limited statistical background, it covers experimental design, data visualization, and advanced numerical methods.
4.6
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Instructors:
English
English
What you'll learn
Master fundamental concepts of statistical inference and sampling distributions
Develop proficiency in hypothesis testing and p-value interpretation
Learn to perform and interpret ANOVA and regression analyses
Gain practical skills in data visualization using R
Understand experimental design and power analysis
Master numerical methods including simulations and bootstrap techniques
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course offers comprehensive training in statistical inference and modeling using R programming. Students learn fundamental concepts of statistical analysis, including sampling distributions, hypothesis testing, and multivariate analysis. The curriculum combines theoretical understanding with practical implementation, covering experimental design, data visualization, and advanced statistical methods. Through hands-on exercises and real-world applications, participants develop skills in both statistical theory and R programming.
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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